diff --git a/cybersecurity_eda.ipynb b/cybersecurity_eda.ipynb
new file mode 100644
index 0000000..8bcadac
--- /dev/null
+++ b/cybersecurity_eda.ipynb
@@ -0,0 +1,5535 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 1. Setup and Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ Libraries imported successfully!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'c:\\Users\\icon\\Downloads\\Project' is not recognized as an internal or external command,\n",
+      "operable program or batch file.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from collections import Counter\n",
+    "from scipy import stats\n",
+    "import sys\n",
+    "!{sys.executable} -m pip install scipy scikit-learn\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set visualization style\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams['figure.figsize'] = (15, 8)\n",
+    "plt.rcParams['font.size'] = 10\n",
+    "\n",
+    "print(\"✓ Libraries imported successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\icon\\Downloads\\Project 1\\Project 1\\my_project\\venv\\Scripts\\python.exe\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "print(sys.executable)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Shape: (40000, 25)\n",
+      "Total Records: 40,000\n",
+      "Total Features: 25\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load dataset\n",
+    "# TODO: Update the filepath to your actual CSV file location\n",
+    "df = pd.read_csv(\"cybersecurity_attacks.csv\")\n",
+    "\n",
+    "print(f\"Dataset Shape: {df.shape}\")\n",
+    "print(f\"Total Records: {df.shape[0]:,}\")\n",
+    "print(f\"Total Features: {df.shape[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Timestamp",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Source IP Address",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Destination IP Address",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Source Port",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Destination Port",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Protocol",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Packet Length",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Packet Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Traffic Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Payload Data",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Malware Indicators",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Anomaly Scores",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Alerts/Warnings",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Attack Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Attack Signature",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Action Taken",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Severity Level",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "User Information",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Device Information",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Network Segment",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Geo-location Data",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Proxy Information",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Firewall Logs",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "IDS/IPS Alerts",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Log Source",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "40f8b6b7-a56f-4dae-ba37-428d319ebb4e",
+       "rows": [
+        [
+         "0",
+         "2023-05-30 06:33:58",
+         "103.216.15.12",
+         "84.9.164.252",
+         "31225",
+         "17616",
+         "ICMP",
+         "503",
+         "Data",
+         "HTTP",
+         "Qui natus odio asperiores nam. Optio nobis iusto accusamus ad perferendis esse at. Asperiores neque at ad.\nMaiores possimus ipsum saepe vitae. Ad possimus veritatis.",
+         "IoC Detected",
+         "28.67",
+         null,
+         "Malware",
+         "Known Pattern B",
+         "Logged",
+         "Low",
+         "Reyansh Dugal",
+         "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.2; Trident/5.0)",
+         "Segment A",
+         "Jamshedpur, Sikkim",
+         "150.9.97.135",
+         "Log Data",
+         null,
+         "Server"
+        ],
+        [
+         "1",
+         "2020-08-26 07:08:30",
+         "78.199.217.198",
+         "66.191.137.154",
+         "17245",
+         "48166",
+         "ICMP",
+         "1174",
+         "Data",
+         "HTTP",
+         "Aperiam quos modi officiis veritatis rem. Omnis nulla dolore perspiciatis.\nIllo animi mollitia vero voluptates error ad. Quidem maxime eaque optio a. Consectetur quasi veniam et totam culpa ullam.",
+         "IoC Detected",
+         "51.5",
+         null,
+         "Malware",
+         "Known Pattern A",
+         "Blocked",
+         "Low",
+         "Sumer Rana",
+         "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)",
+         "Segment B",
+         "Bilaspur, Nagaland",
+         null,
+         "Log Data",
+         null,
+         "Firewall"
+        ],
+        [
+         "2",
+         "2022-11-13 08:23:25",
+         "63.79.210.48",
+         "198.219.82.17",
+         "16811",
+         "53600",
+         "UDP",
+         "306",
+         "Control",
+         "HTTP",
+         "Perferendis sapiente vitae soluta. Hic delectus quae nemo ea esse est rerum.",
+         "IoC Detected",
+         "87.42",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "Low",
+         "Himmat Karpe",
+         "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.2; Trident/5.0)",
+         "Segment C",
+         "Bokaro, Rajasthan",
+         "114.133.48.179",
+         "Log Data",
+         "Alert Data",
+         "Firewall"
+        ],
+        [
+         "3",
+         "2023-07-02 10:38:46",
+         "163.42.196.10",
+         "101.228.192.255",
+         "20018",
+         "32534",
+         "UDP",
+         "385",
+         "Data",
+         "HTTP",
+         "Totam maxime beatae expedita explicabo porro labore. Minima ab fugit officiis dicta perspiciatis pariatur. Facilis voluptates eligendi dolores eveniet deserunt. Eveniet reprehenderit culpa quo.",
+         null,
+         "15.79",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Blocked",
+         "Medium",
+         "Fateh Kibe",
+         "Mozilla/5.0 (Macintosh; PPC Mac OS X 10_11_5; rv:1.9.6.20) Gecko/2583-02-14 13:30:10 Firefox/11.0",
+         "Segment B",
+         "Jaunpur, Rajasthan",
+         null,
+         null,
+         "Alert Data",
+         "Firewall"
+        ],
+        [
+         "4",
+         "2023-07-16 13:11:07",
+         "71.166.185.76",
+         "189.243.174.238",
+         "6131",
+         "26646",
+         "TCP",
+         "1462",
+         "Data",
+         "DNS",
+         "Odit nesciunt dolorem nisi iste iusto. Animi voluptates soluta quis doloribus quas. Iure harum nihil hic illo repellendus.\nQuia illo fugit eligendi doloremque. In doloremque autem iure.",
+         null,
+         "0.52",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Blocked",
+         "Low",
+         "Dhanush Chad",
+         "Mozilla/5.0 (compatible; MSIE 5.0; Windows NT 6.2; Trident/3.0)",
+         "Segment C",
+         "Anantapur, Tripura",
+         "149.6.110.119",
+         null,
+         "Alert Data",
+         "Firewall"
+        ]
+       ],
+       "shape": {
+        "columns": 25,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>Source IP Address</th>\n",
+       "      <th>Destination IP Address</th>\n",
+       "      <th>Source Port</th>\n",
+       "      <th>Destination Port</th>\n",
+       "      <th>Protocol</th>\n",
+       "      <th>Packet Length</th>\n",
+       "      <th>Packet Type</th>\n",
+       "      <th>Traffic Type</th>\n",
+       "      <th>Payload Data</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Action Taken</th>\n",
+       "      <th>Severity Level</th>\n",
+       "      <th>User Information</th>\n",
+       "      <th>Device Information</th>\n",
+       "      <th>Network Segment</th>\n",
+       "      <th>Geo-location Data</th>\n",
+       "      <th>Proxy Information</th>\n",
+       "      <th>Firewall Logs</th>\n",
+       "      <th>IDS/IPS Alerts</th>\n",
+       "      <th>Log Source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-05-30 06:33:58</td>\n",
+       "      <td>103.216.15.12</td>\n",
+       "      <td>84.9.164.252</td>\n",
+       "      <td>31225</td>\n",
+       "      <td>17616</td>\n",
+       "      <td>ICMP</td>\n",
+       "      <td>503</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Qui natus odio asperiores nam. Optio nobis ius...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Logged</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Reyansh Dugal</td>\n",
+       "      <td>Mozilla/5.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
+       "      <td>Segment A</td>\n",
+       "      <td>Jamshedpur, Sikkim</td>\n",
+       "      <td>150.9.97.135</td>\n",
+       "      <td>Log Data</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Server</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-08-26 07:08:30</td>\n",
+       "      <td>78.199.217.198</td>\n",
+       "      <td>66.191.137.154</td>\n",
+       "      <td>17245</td>\n",
+       "      <td>48166</td>\n",
+       "      <td>ICMP</td>\n",
+       "      <td>1174</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Aperiam quos modi officiis veritatis rem. Omni...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Blocked</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Sumer Rana</td>\n",
+       "      <td>Mozilla/5.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
+       "      <td>Segment B</td>\n",
+       "      <td>Bilaspur, Nagaland</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Log Data</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Firewall</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-11-13 08:23:25</td>\n",
+       "      <td>63.79.210.48</td>\n",
+       "      <td>198.219.82.17</td>\n",
+       "      <td>16811</td>\n",
+       "      <td>53600</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>306</td>\n",
+       "      <td>Control</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Perferendis sapiente vitae soluta. Hic delectu...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Ignored</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Himmat Karpe</td>\n",
+       "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
+       "      <td>Segment C</td>\n",
+       "      <td>Bokaro, Rajasthan</td>\n",
+       "      <td>114.133.48.179</td>\n",
+       "      <td>Log Data</td>\n",
+       "      <td>Alert Data</td>\n",
+       "      <td>Firewall</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-07-02 10:38:46</td>\n",
+       "      <td>163.42.196.10</td>\n",
+       "      <td>101.228.192.255</td>\n",
+       "      <td>20018</td>\n",
+       "      <td>32534</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>385</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Totam maxime beatae expedita explicabo porro l...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Blocked</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Fateh Kibe</td>\n",
+       "      <td>Mozilla/5.0 (Macintosh; PPC Mac OS X 10_11_5; ...</td>\n",
+       "      <td>Segment B</td>\n",
+       "      <td>Jaunpur, Rajasthan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Alert Data</td>\n",
+       "      <td>Firewall</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-07-16 13:11:07</td>\n",
+       "      <td>71.166.185.76</td>\n",
+       "      <td>189.243.174.238</td>\n",
+       "      <td>6131</td>\n",
+       "      <td>26646</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>1462</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>DNS</td>\n",
+       "      <td>Odit nesciunt dolorem nisi iste iusto. Animi v...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Blocked</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Dhanush Chad</td>\n",
+       "      <td>Mozilla/5.0 (compatible; MSIE 5.0; Windows NT ...</td>\n",
+       "      <td>Segment C</td>\n",
+       "      <td>Anantapur, Tripura</td>\n",
+       "      <td>149.6.110.119</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Alert Data</td>\n",
+       "      <td>Firewall</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Timestamp Source IP Address Destination IP Address  Source Port  \\\n",
+       "0  2023-05-30 06:33:58     103.216.15.12           84.9.164.252        31225   \n",
+       "1  2020-08-26 07:08:30    78.199.217.198         66.191.137.154        17245   \n",
+       "2  2022-11-13 08:23:25      63.79.210.48          198.219.82.17        16811   \n",
+       "3  2023-07-02 10:38:46     163.42.196.10        101.228.192.255        20018   \n",
+       "4  2023-07-16 13:11:07     71.166.185.76        189.243.174.238         6131   \n",
+       "\n",
+       "   Destination Port Protocol  Packet Length Packet Type Traffic Type  \\\n",
+       "0             17616     ICMP            503        Data         HTTP   \n",
+       "1             48166     ICMP           1174        Data         HTTP   \n",
+       "2             53600      UDP            306     Control         HTTP   \n",
+       "3             32534      UDP            385        Data         HTTP   \n",
+       "4             26646      TCP           1462        Data          DNS   \n",
+       "\n",
+       "                                        Payload Data  ... Action Taken  \\\n",
+       "0  Qui natus odio asperiores nam. Optio nobis ius...  ...       Logged   \n",
+       "1  Aperiam quos modi officiis veritatis rem. Omni...  ...      Blocked   \n",
+       "2  Perferendis sapiente vitae soluta. Hic delectu...  ...      Ignored   \n",
+       "3  Totam maxime beatae expedita explicabo porro l...  ...      Blocked   \n",
+       "4  Odit nesciunt dolorem nisi iste iusto. Animi v...  ...      Blocked   \n",
+       "\n",
+       "   Severity Level User Information  \\\n",
+       "0             Low    Reyansh Dugal   \n",
+       "1             Low       Sumer Rana   \n",
+       "2             Low     Himmat Karpe   \n",
+       "3          Medium       Fateh Kibe   \n",
+       "4             Low     Dhanush Chad   \n",
+       "\n",
+       "                                  Device Information Network Segment  \\\n",
+       "0  Mozilla/5.0 (compatible; MSIE 8.0; Windows NT ...       Segment A   \n",
+       "1  Mozilla/5.0 (compatible; MSIE 8.0; Windows NT ...       Segment B   \n",
+       "2  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...       Segment C   \n",
+       "3  Mozilla/5.0 (Macintosh; PPC Mac OS X 10_11_5; ...       Segment B   \n",
+       "4  Mozilla/5.0 (compatible; MSIE 5.0; Windows NT ...       Segment C   \n",
+       "\n",
+       "    Geo-location Data Proxy Information Firewall Logs IDS/IPS Alerts  \\\n",
+       "0  Jamshedpur, Sikkim      150.9.97.135      Log Data            NaN   \n",
+       "1  Bilaspur, Nagaland               NaN      Log Data            NaN   \n",
+       "2   Bokaro, Rajasthan    114.133.48.179      Log Data     Alert Data   \n",
+       "3  Jaunpur, Rajasthan               NaN           NaN     Alert Data   \n",
+       "4  Anantapur, Tripura     149.6.110.119           NaN     Alert Data   \n",
+       "\n",
+       "  Log Source  \n",
+       "0     Server  \n",
+       "1   Firewall  \n",
+       "2   Firewall  \n",
+       "3   Firewall  \n",
+       "4   Firewall  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display first few rows\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 40000 entries, 0 to 39999\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column                  Non-Null Count  Dtype  \n",
+      "---  ------                  --------------  -----  \n",
+      " 0   Timestamp               40000 non-null  object \n",
+      " 1   Source IP Address       40000 non-null  object \n",
+      " 2   Destination IP Address  40000 non-null  object \n",
+      " 3   Source Port             40000 non-null  int64  \n",
+      " 4   Destination Port        40000 non-null  int64  \n",
+      " 5   Protocol                40000 non-null  object \n",
+      " 6   Packet Length           40000 non-null  int64  \n",
+      " 7   Packet Type             40000 non-null  object \n",
+      " 8   Traffic Type            40000 non-null  object \n",
+      " 9   Payload Data            40000 non-null  object \n",
+      " 10  Malware Indicators      20000 non-null  object \n",
+      " 11  Anomaly Scores          40000 non-null  float64\n",
+      " 12  Alerts/Warnings         19933 non-null  object \n",
+      " 13  Attack Type             40000 non-null  object \n",
+      " 14  Attack Signature        40000 non-null  object \n",
+      " 15  Action Taken            40000 non-null  object \n",
+      " 16  Severity Level          40000 non-null  object \n",
+      " 17  User Information        40000 non-null  object \n",
+      " 18  Device Information      40000 non-null  object \n",
+      " 19  Network Segment         40000 non-null  object \n",
+      " 20  Geo-location Data       40000 non-null  object \n",
+      " 21  Proxy Information       20149 non-null  object \n",
+      " 22  Firewall Logs           20039 non-null  object \n",
+      " 23  IDS/IPS Alerts          19950 non-null  object \n",
+      " 24  Log Source              40000 non-null  object \n",
+      "dtypes: float64(1), int64(3), object(21)\n",
+      "memory usage: 7.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Data types and basic info\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "MISSING VALUES AND DISTINCTNESS ANALYSIS\n",
+      "================================================================================\n",
+      "                        Missing_Count  Percentage  Distinct_Count  \\\n",
+      "Alerts/Warnings                 20067     50.1675               1   \n",
+      "IDS/IPS Alerts                  20050     50.1250               1   \n",
+      "Malware Indicators              20000     50.0000               1   \n",
+      "Firewall Logs                   19961     49.9025               1   \n",
+      "Proxy Information               19851     49.6275           20148   \n",
+      "Source IP Address                   0      0.0000           40000   \n",
+      "Destination IP Address              0      0.0000           40000   \n",
+      "Source Port                         0      0.0000           29761   \n",
+      "Timestamp                           0      0.0000           39997   \n",
+      "Traffic Type                        0      0.0000               3   \n",
+      "Packet Type                         0      0.0000               2   \n",
+      "Packet Length                       0      0.0000            1437   \n",
+      "Protocol                            0      0.0000               3   \n",
+      "Destination Port                    0      0.0000           29895   \n",
+      "Attack Type                         0      0.0000               3   \n",
+      "Payload Data                        0      0.0000           40000   \n",
+      "Anomaly Scores                      0      0.0000            9826   \n",
+      "Severity Level                      0      0.0000               3   \n",
+      "Action Taken                        0      0.0000               3   \n",
+      "Attack Signature                    0      0.0000               2   \n",
+      "User Information                    0      0.0000           32389   \n",
+      "Geo-location Data                   0      0.0000            8723   \n",
+      "Network Segment                     0      0.0000               3   \n",
+      "Device Information                  0      0.0000           32104   \n",
+      "Log Source                          0      0.0000               2   \n",
+      "\n",
+      "                        Distinct_Percentage  \n",
+      "Alerts/Warnings                      0.0025  \n",
+      "IDS/IPS Alerts                       0.0025  \n",
+      "Malware Indicators                   0.0025  \n",
+      "Firewall Logs                        0.0025  \n",
+      "Proxy Information                   50.3700  \n",
+      "Source IP Address                  100.0000  \n",
+      "Destination IP Address             100.0000  \n",
+      "Source Port                         74.4025  \n",
+      "Timestamp                           99.9925  \n",
+      "Traffic Type                         0.0075  \n",
+      "Packet Type                          0.0050  \n",
+      "Packet Length                        3.5925  \n",
+      "Protocol                             0.0075  \n",
+      "Destination Port                    74.7375  \n",
+      "Attack Type                          0.0075  \n",
+      "Payload Data                       100.0000  \n",
+      "Anomaly Scores                      24.5650  \n",
+      "Severity Level                       0.0075  \n",
+      "Action Taken                         0.0075  \n",
+      "Attack Signature                     0.0050  \n",
+      "User Information                    80.9725  \n",
+      "Geo-location Data                   21.8075  \n",
+      "Network Segment                      0.0075  \n",
+      "Device Information                  80.2600  \n",
+      "Log Source                           0.0050  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Missing values analysis\n",
+    "missing_df = pd.DataFrame({\n",
+    "    'Missing_Count': df.isnull().sum(),\n",
+    "    'Percentage': (df.isnull().sum() / len(df)) * 100,\n",
+    "    'Distinct_Count': df.nunique(),\n",
+    "    'Distinct_Percentage': (df.nunique() / len(df)) * 100\n",
+    "}).sort_values('Missing_Count', ascending=False)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"MISSING VALUES AND DISTINCTNESS ANALYSIS\")\n",
+    "print(\"=\"*80)\n",
+    "print(missing_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Attack Type Distribution:\n",
+      "Attack Type\n",
+      "DDoS         13428\n",
+      "Malware      13307\n",
+      "Intrusion    13265\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Attack Type distribution\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    print(\"\\nAttack Type Distribution:\")\n",
+    "    print(df['Attack Type'].value_counts())\n",
+    "    \n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    df['Attack Type'].value_counts().plot(kind='bar', color='steelblue', edgecolor='black')\n",
+    "    plt.title('Attack Type Distribution', fontsize=14, fontweight='bold')\n",
+    "    plt.xlabel('Attack Type')\n",
+    "    plt.ylabel('Count')\n",
+    "    plt.xticks(rotation=45, ha='right')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 2. PART 1: Proxy Information Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================================\n",
+      "PROXY INFORMATION ANALYSIS\n",
+      "================================================================================\n",
+      "\n",
+      "Proxy Information Statistics:\n",
+      "  - Total records: 40,000\n",
+      "  - Records WITH proxy info: 20,149 (50.37%)\n",
+      "  - Records WITHOUT proxy info: 19,851 (49.63%)\n",
+      "  - Unique proxy values: 20,148\n",
+      "\n",
+      "Proxy Usage Distribution:\n",
+      "has_proxy\n",
+      "1    20149\n",
+      "0    19851\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Proxy Information Analysis\n",
+    "print(\"=\"*80)\n",
+    "print(\"PROXY INFORMATION ANALYSIS\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "if 'Proxy Information' in df.columns:\n",
+    "    # Basic statistics\n",
+    "    total_records = len(df)\n",
+    "    proxy_present = df['Proxy Information'].notna().sum()\n",
+    "    proxy_missing = df['Proxy Information'].isna().sum()\n",
+    "    unique_proxies = df['Proxy Information'].nunique()\n",
+    "    \n",
+    "    print(f\"\\nProxy Information Statistics:\")\n",
+    "    print(f\"  - Total records: {total_records:,}\")\n",
+    "    print(f\"  - Records WITH proxy info: {proxy_present:,} ({proxy_present/total_records*100:.2f}%)\")\n",
+    "    print(f\"  - Records WITHOUT proxy info: {proxy_missing:,} ({proxy_missing/total_records*100:.2f}%)\")\n",
+    "    print(f\"  - Unique proxy values: {unique_proxies:,}\")\n",
+    "    \n",
+    "    # Create binary feature: has_proxy\n",
+    "    df['has_proxy'] = df['Proxy Information'].notna().astype(int)\n",
+    "    \n",
+    "    print(f\"\\nProxy Usage Distribution:\")\n",
+    "    print(df['has_proxy'].value_counts())\n",
+    "else:\n",
+    "    print(\"Warning: 'Proxy Information' column not found!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Proxy Usage by Attack Type:\n",
+      "has_proxy            0          1\n",
+      "Attack Type                      \n",
+      "DDoS         49.471254  50.528746\n",
+      "Intrusion    50.267622  49.732378\n",
+      "Malware      49.147065  50.852935\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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nQHOSg0tPockwQHxwoJJ/DCgJSC5DoyQ21FNj+faePXuKEnOffvqpeByPNEFCA8D/hg8fTh07dhRlpTj4yslJnknJASv+7ObyUnw88w8uHqBw3HHH0apVq6i4uFgkNrgsFQDsx99hFy9eLL7v/uIXvxDX/e53v6MLLriA7rzzTvG9l2dicNkp/v6rfKYqn6eZmZlYlQawbt2llPjl727cB/HDDz8U1/FnAyc2+N+ff/45nXvuuWI/Vn6PYd/FuvU6zsAlcHlWKX8n4e8sbOTIkTR58mTRWJmv432Xv8dcccUV2HexXn2x3/J5lgcU3XHHHeK7NSfelixZIs6pnODgSia8X/N3ck7WKSXYcc7Fug0CJDUg8PiLw9ChQ0UfDP7SW1dXJ+rvH3HEEWLU99tvvy1Go/G/+Ysx/+DjqXd80macpf7oo4/EiZxHfHO9bvVINgCIj2OPPVaM2uMyGvfee29EYkNx5plnih8PHHTgL10cDAUA/+Mycdz4m5OTfBxz0+Lrr79eBFevvvpqcR+lbwZf5ubhVVVVYnACEhoA4XgQDn8OLl++vCVwwd+Fn3vuObr44otFKSr+3jto0KCwZqAorxob1q27lIAZz9Y79NBDRXm0OXPmiO96/P2OExunnXaa+D2nhn0X69ZLvH/yPsnnXXUZXB6swYOsNm3aJC5j38V69dt+y7+VebAQD+Bl/J16w4YNdPPNN4uEx1FHHSViY1OmTAn7zY1zLtZtEKBpAAQafxmeMWOGSEYoXy6UafRcK5BrtPK0Om6CyOUtOLGhfNngxqT8I49r9PP127dvFyNEua8GAHiX2PjHP/4hAprcRFjpsaF8weKpsbfddptoDM5BByQ0AIKBj2U+jrm+/wknnCBGkHOpKR6IwL0AeHQ5H/c80pHLOHBglkvrvPrqqyIoCwCRxxSXUeXABI+65OOLZxpzrxqe+chlHZXvvep+BSB3vsK6dY+yL3Jyo3///vSHP/yBRo8eLRIbPIuPR8Nz8Bj7LNatH/FvDz6/8vcX3ke5LxiXvOZ4g3I7YL36Ce+TPAg4JydHXObv15xQ5oFF/H2Bv0fwACIl6YFzL9ZtkKCnBgTeDz/8QH/+85/F9HqepswjPnlk2tq1a+npp58W0+l4NgefqHkUBX9x5hM5Z6aVkUI84oIz0agrDOAPfMzyjI177rlH1FNmPD2WZ3C89dZb4g8JSIDgHdc8EOE3v/kNbd26VZSRY2+++ab4/P373/8uSkNy4vLnP/+514sL4DtffvmlCPhmZ2fT0UcfLQbocA1sHunO/WqUxAbPOObPTB7Ew2VS+D6AdeuHfZeDapww4llF3BeNB6DxLD7G+zD/ruOa7sqMDXVfNcC69XLf5UQb9/3jpuAcZ+D+RUp1h+nTp1NRUVFYX869e/eKfRiwXr3ebydMmCDK+ikJN/5+wDhOxiorK0U/O96PuaoJYN0GCWrsQOBx81CehcENwflH3ieffCKCJdw7QzlRcykqdsstt4gRQBwwYTzCggMpPOIbAPw3Y4MTG5xw5NElnNDgUnFcsgYJDYDg4KAUj3zmhrDnnHOOKD/FeIYkf3Zz4JUTlTxjg4O03A+Af4Dx6EcA2I+DZf/73/9EX6lly5bRMcccI44fLjN19tlni940PGBHCbJxgI0/O5XgBRjDuo3v+h0/frxIbvMsvHbt2rUk4rg/DN9XKUXFybi2bdti18W69c2+y79PLrroIpGYU9OW6eHfLFzWh2MOGDSJ9er1fst9NDhhwYk4LkOlxMgYn3/5+/bhhx9Ou3btivuyBg3Wrf+g/BQkVGKDR3XzyE/+N08DZRxI4S8TnNjgUaDctEuBLxkAwShFxU1OuTH4iy++KErTAEBwcHCKZ0Zy0EopKafUoeY+Onz7+eefLwYa/OUvfxElJZHQADjoq6++EqXZHnnkEVGa7fnnn6effvpJzGziZss8A/m9994TQWGu686JRA6y8THHs5EZyknow7qN//rlBve8vyrUnw28Dx922GH0n//8R3wWqD8zAOvW632X4wzcr1M5pyrnVR4Bz9UiGH+vefbZZ0WJTcQasF79sN9yU3BOFiv7KuNZcpzgUAZCcA87ZX/F9wWs2yBBUgMSBtcF5NFqHDjhevtKjw3+UackNkaOHClu5+l3ABCMxMaDDz4ogjfc8wa19QGCq3PnzqJXjlKzl3EJEk5U/vjjjyIAwNAUHCAcz2oqLCykYcOGicsDBgwQxwuPxOSZGNyP4LXXXqPi4mIxW4NHwfPMp3nz5tGpp54qHoMSPvqwbr1Zvzxzj8tPKZTfa4xLBZ9yyiniOyAa1WLd+nXf5XOqcl7l/ZQTyP/85z9F+WtOyimPA6xXP+23jJMZ/Lv6kksuEbM8uZrJd99911L6Fd8XsG6DBEkNSCg8FZSnhP3rX/8SoyjUiQ01pZcGAPgf/6jlgCd/KQMA/1NGePHIME5i8Egxri3N/TS4txX3wOIRYhzA4s9nLi/CJRp4ZhbDjymAcB06dBDfaVesWNFyXY8ePVp6wjEuM/Hyyy/TpEmTxL/5Ma+88kpLvwLQh3Xr3frVnut5X+ZSKIw/L5RZ94B16/d9t0uXLmJmEc+ae+mll2jw4MEeLHEwYL16v355tgYnMPj7N/eI4ZnS/P2BZ34C1m3QoKcGJOSMDS5Dde2114ppdJyZRhkLgGDjJmcA4G9KQ1f+42nuPPqLZ0hyOZw33nhDNH/l8lJ8PY/C5VFkfF9uZjht2jQxkwMA9lNmFfNAHA5IjBs3ThwvSjKQA74cAObgBc944uOPZzmdddZZ4k+5H0TCuvXX+lXvp0opFMC6DdK+y43teaQ799LAICys1yB8X+Dv3FyaXZm5oZSlAqzboMG3BkjYHhu33nqryDjn5OR4vTgAAAAJPdWd+2MoI8C2bdsmakpzQ3BuDL5lyxY6/vjj6Ze//KUYzcifzVwuUmlIyGVzevXq5fG7APCPJ554Qoyy3LhxoziGOEDBZaU4KKEE0TiIwQELJZHI1/NIYW4AygN8MOMJ6zYo+y7jGvC87/JvOMC6DdK+y9UhuNwPz9DAQEqs1yDst3w977c8q4O/LyCZjHUbZBi+AwnryCOPFKUs+MSNZkcAAADO41ILv//970XtfgWXmuIfT2eeeaZIcHC5KR45fswxx9DDDz9MpaWldOGFF4oZlfzXp08fbBqAAzjh98wzz4iSqtxH6v3336c///nP9Pnnn4vAA3+n5eOLS0bw6Mrs7GzxXfeBBx6g6667TiQYGZIakbBu/bt++bOAA2yAdRu0fff6668XI+GR0MB6Ddp+i+8LWLeJADM1IKEpCQ38sAMAAHAW98VYsGABLVy4UDQgrK2tpaOPPloEprp160YfffSRKAfJfXG4CSGPFOMyVDzl/fzzzxfPgc9ogHDLli2j6dOni5JsjI8vHlH5t7/9TQQlfvazn4nvtVwqgoMWmZmZIln47LPPisaffOyBPqxbd2H9Yt0GFfZdrNcgwn6LdQtIakASQEIDAADAea1btxZNiXlE2IYNG+i9994T9XyPOOII0QeHy0+deuqpIqHBOCDLTQjVvTPwGQ1wEJeL2LlzZ9iIdR6ByTWwOYnB9a/52DrqqKOoXbt2osknH2fz588XZd2GDBmC1WkA69ZdWL9Yt0GFfRfrNYiw32Ldwn4oPwUAAAAApvB0dvbzn/+cpkyZIgKumzdvpv/85z+0fPly0SyTp8KXlJSIUeY//vgjPfroo7R+/Xo00QTQWLVqFa1Zs4b27dsnZjG9+eabYhaUonv37nT22WfT4YcfTrNmzRLHUVZWlkgU8rHFfWmQ0NCHdesurF+s26DCvov1GkTYb7FuIVyoGc0GAAAAAMCkuro6ysjIEDM1vv/+exo1apQogcM1e7nPBjcF55q9HICtqqqinJwcuvPOO0WyAwD249rWn3zyiUho8CwM7gnHCQue/TRjxgzq169fy6ri44xna/D13Az0008/FbXc+/bti9WpA+vWXVi/WLdBhX0X6zWIsN9i3UIkJDUAAAAAIKaZM2dSSkqKaPitHhW+e/duuuyyy+i0004T9f6vueYakcC44ooraODAgaJxON+HS+i0adMGaxrgAJ7Z9Mgjj4hji5OE69ato/vuu4+OO+44qqmpEcmN3/3ud2FJC57JwX0zuMcGGMO6dRfWL9ZtUGHfxXoNIuy3WLegD+WnAAAAACCqbdu2icDrQw89JEaK3XTTTSIIyzV9OVlxww03iB9clZWVdOONN4pR50888QR9+eWXIpHRq1cvJDQANLjk1IQJE2jYsGE0ZswY8e+hQ4dSp06dRD+asrIycbwtXry45TH/3959wFZZd3EcP4WWoahlWYOAio3FigqKAUUsUBmKsoJiGAZRIiqIggiCyrKQSqlEEIzSqOzpYisqw9QRCZLgYqkgKKCoQUH2m98xz33vfVltoaUP7/eT3LTc3vHc04ek/+f8zznly5f3KiicGLEtXMSX2IYV5y5xDSPOW2KLY4s/zv0AAACA00VWtZbSLnENJ9ZFVs3SaNeunTVs2NAvxKalpVlubq516dLF+vXr54mPhQsX+sVatdUB8C91/42Li/OZM5qLEahYsaInLFasWOEt3EqXLm1Lliyxbt26Wb169TyRqFkbaj+FYyO2hYv4Etuw4twlrmHEeUtscWJUagAAAOCkdFE1JyfH+/jrgqtaTSm5ofY4uvBaqVIlmzFjhld1qD1VRkaGX3wloQHEUkJDVI2xZcsW2759ux06dMjvU1Jj3759dvjwYW/11r9/fxsxYoRVqVLF27nNmjXLkpOTCelxENvCRXyJbVhx7hLXMOK8JbY4MSo1AAAAkCcNGjSw7OxsGzBggA/9VrWGhhyPGjXKmjZtatu2bbMJEyb4gHBdgAVwfOnp6Z4AVIVGcOGiZMmSflMFhyo1zj//fEtJSbFmzZoRynwgtoWL+BLbsOLcJa5hxHlLbHFsVGoAAAAgzzTEODMz0/r06WNfffW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+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize proxy usage patterns\n",
+    "if 'has_proxy' in df.columns:\n",
+    "    fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "    \n",
+    "    # 1. Overall proxy usage pie chart\n",
+    "    proxy_counts = df['has_proxy'].value_counts()\n",
+    "    labels = ['No Proxy', 'With Proxy']\n",
+    "    colors = ['lightcoral', 'lightgreen']\n",
+    "    axes[0, 0].pie(proxy_counts.values, labels=labels, autopct='%1.1f%%', \n",
+    "                    colors=colors, startangle=90)\n",
+    "    axes[0, 0].set_title('Overall Proxy Usage Distribution', fontsize=14, fontweight='bold')\n",
+    "    \n",
+    "    # 2. Proxy usage by Attack Type\n",
+    "    if 'Attack Type' in df.columns:\n",
+    "        proxy_attack = pd.crosstab(df['Attack Type'], df['has_proxy'], normalize='index') * 100\n",
+    "        proxy_attack.plot(kind='bar', ax=axes[0, 1], stacked=False, \n",
+    "                         color=['lightcoral', 'lightgreen'])\n",
+    "        axes[0, 1].set_title('Proxy Usage by Attack Type (%)', fontsize=14, fontweight='bold')\n",
+    "        axes[0, 1].set_xlabel('Attack Type')\n",
+    "        axes[0, 1].set_ylabel('Percentage')\n",
+    "        axes[0, 1].legend(['No Proxy', 'With Proxy'])\n",
+    "        axes[0, 1].tick_params(axis='x', rotation=45)\n",
+    "        \n",
+    "        # Print statistical summary\n",
+    "        print(\"\\nProxy Usage by Attack Type:\")\n",
+    "        print(proxy_attack)\n",
+    "    \n",
+    "    # 3. Proxy usage by Severity Level\n",
+    "    if 'Severity Level' in df.columns:\n",
+    "        proxy_severity = pd.crosstab(df['Severity Level'], df['has_proxy'])\n",
+    "        proxy_severity.plot(kind='bar', ax=axes[1, 0], color=['lightcoral', 'lightgreen'])\n",
+    "        axes[1, 0].set_title('Proxy Usage by Severity Level', fontsize=14, fontweight='bold')\n",
+    "        axes[1, 0].set_xlabel('Severity Level')\n",
+    "        axes[1, 0].set_ylabel('Count')\n",
+    "        axes[1, 0].legend(['No Proxy', 'With Proxy'])\n",
+    "        axes[1, 0].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    # 4. Proxy usage over time\n",
+    "    if 'Timestamp' in df.columns:\n",
+    "        df_temp = df.copy()\n",
+    "        df_temp['Timestamp'] = pd.to_datetime(df_temp['Timestamp'], errors='coerce')\n",
+    "        df_temp = df_temp.dropna(subset=['Timestamp'])\n",
+    "        df_temp['Date'] = df_temp['Timestamp'].dt.date\n",
+    "        \n",
+    "        proxy_time = df_temp.groupby('Date')['has_proxy'].agg(['sum', 'count'])\n",
+    "        proxy_time['percentage'] = (proxy_time['sum'] / proxy_time['count']) * 100\n",
+    "        \n",
+    "        axes[1, 1].plot(proxy_time.index, proxy_time['percentage'], \n",
+    "                       marker='o', color='steelblue', linewidth=2)\n",
+    "        axes[1, 1].set_title('Proxy Usage Trend Over Time', fontsize=14, fontweight='bold')\n",
+    "        axes[1, 1].set_xlabel('Date')\n",
+    "        axes[1, 1].set_ylabel('Percentage Using Proxy')\n",
+    "        axes[1, 1].tick_params(axis='x', rotation=45)\n",
+    "        axes[1, 1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Proxy Usage by Log Source:\n",
+      "has_proxy           0          1\n",
+      "Log Source                      \n",
+      "Firewall    49.482999  50.517001\n",
+      "Server      49.773687  50.226313\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Analyze relationship with Log Source (Firewall vs Server)\n",
+    "if 'Log Source' in df.columns and 'has_proxy' in df.columns:\n",
+    "    print(\"\\nProxy Usage by Log Source:\")\n",
+    "    log_proxy = pd.crosstab(df['Log Source'], df['has_proxy'], normalize='index') * 100\n",
+    "    print(log_proxy)\n",
+    "    \n",
+    "    # Visualize\n",
+    "    log_proxy.plot(kind='bar', figsize=(10, 6), color=['lightcoral', 'lightgreen'])\n",
+    "    plt.title('Proxy Usage: Firewall vs Server Logs', fontsize=14, fontweight='bold')\n",
+    "    plt.xlabel('Log Source')\n",
+    "    plt.ylabel('Percentage')\n",
+    "    plt.legend(['No Proxy', 'With Proxy'])\n",
+    "    plt.xticks(rotation=0)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "PROXY USAGE INSIGHTS BY ATTACK TYPE\n",
+      "================================================================================\n",
+      "\n",
+      "Malware:\n",
+      "  - Total attacks: 13,307\n",
+      "  - With proxy: 6,767 (50.85%)\n",
+      "  - Without proxy: 6,540 (49.15%)\n",
+      "\n",
+      "DDoS:\n",
+      "  - Total attacks: 13,428\n",
+      "  - With proxy: 6,785 (50.53%)\n",
+      "  - Without proxy: 6,643 (49.47%)\n",
+      "\n",
+      "Intrusion:\n",
+      "  - Total attacks: 13,265\n",
+      "  - With proxy: 6,597 (49.73%)\n",
+      "  - Without proxy: 6,668 (50.27%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Statistical significance test\n",
+    "if 'Attack Type' in df.columns and 'has_proxy' in df.columns:\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"PROXY USAGE INSIGHTS BY ATTACK TYPE\")\n",
+    "    print(\"=\"*80)\n",
+    "    \n",
+    "    for attack_type in df['Attack Type'].unique():\n",
+    "        subset = df[df['Attack Type'] == attack_type]\n",
+    "        proxy_pct = (subset['has_proxy'].sum() / len(subset)) * 100\n",
+    "        \n",
+    "        print(f\"\\n{attack_type}:\")\n",
+    "        print(f\"  - Total attacks: {len(subset):,}\")\n",
+    "        print(f\"  - With proxy: {subset['has_proxy'].sum():,} ({proxy_pct:.2f}%)\")\n",
+    "        print(f\"  - Without proxy: {len(subset) - subset['has_proxy'].sum():,} ({100-proxy_pct:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 3. PART 2: IP Trends and Spoofing Detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================================\n",
+      "IP TRENDS AND SPOOFING DETECTION\n",
+      "================================================================================\n",
+      "\n",
+      "IP Address Statistics:\n",
+      "  - Unique Source IPs: 40,000\n",
+      "  - Unique Destination IPs: 40,000\n",
+      "  - Total IP-to-IP connections: 40,000\n",
+      "  - Average connections per source IP: 1.00\n",
+      "  - Average connections per destination IP: 1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"=\"*80)\n",
+    "print(\"IP TRENDS AND SPOOFING DETECTION\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "# Basic IP statistics\n",
+    "if 'Source IP Address' in df.columns and 'Destination IP Address' in df.columns:\n",
+    "    print(f\"\\nIP Address Statistics:\")\n",
+    "    print(f\"  - Unique Source IPs: {df['Source IP Address'].nunique():,}\")\n",
+    "    print(f\"  - Unique Destination IPs: {df['Destination IP Address'].nunique():,}\")\n",
+    "    print(f\"  - Total IP-to-IP connections: {len(df):,}\")\n",
+    "    print(f\"  - Average connections per source IP: {len(df)/df['Source IP Address'].nunique():.2f}\")\n",
+    "    print(f\"  - Average connections per destination IP: {len(df)/df['Destination IP Address'].nunique():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "TOP SOURCE IP ADDRESSES\n",
+      "================================================================================\n",
+      "\n",
+      "Top 20 Source IPs:\n",
+      "Source IP Address\n",
+      "103.216.15.12      1\n",
+      "78.199.217.198     1\n",
+      "63.79.210.48       1\n",
+      "163.42.196.10      1\n",
+      "71.166.185.76      1\n",
+      "198.102.5.160      1\n",
+      "97.253.103.59      1\n",
+      "11.48.99.245       1\n",
+      "49.32.208.167      1\n",
+      "114.109.149.113    1\n",
+      "177.21.83.200      1\n",
+      "92.4.25.171        1\n",
+      "57.91.207.84       1\n",
+      "80.28.21.123       1\n",
+      "54.163.130.178     1\n",
+      "130.163.192.252    1\n",
+      "4.255.187.165      1\n",
+      "212.164.196.41     1\n",
+      "119.101.120.119    1\n",
+      "104.176.150.78     1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Top Source IPs\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"TOP SOURCE IP ADDRESSES\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "top_src_ips = df['Source IP Address'].value_counts().head(20)\n",
+    "print(\"\\nTop 20 Source IPs:\")\n",
+    "print(top_src_ips)\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.barh(range(len(top_src_ips)), top_src_ips.values, color='steelblue')\n",
+    "plt.yticks(range(len(top_src_ips)), top_src_ips.index)\n",
+    "plt.xlabel('Frequency (Number of Connections)', fontsize=12)\n",
+    "plt.ylabel('Source IP Address', fontsize=12)\n",
+    "plt.title('Top 20 Most Active Source IP Addresses', fontsize=14, fontweight='bold')\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "TOP DESTINATION IP ADDRESSES\n",
+      "================================================================================\n",
+      "\n",
+      "Top 20 Destination IPs:\n",
+      "Destination IP Address\n",
+      "84.9.164.252       1\n",
+      "66.191.137.154     1\n",
+      "198.219.82.17      1\n",
+      "101.228.192.255    1\n",
+      "189.243.174.238    1\n",
+      "147.190.155.133    1\n",
+      "77.16.101.53       1\n",
+      "178.157.14.116     1\n",
+      "72.202.237.9       1\n",
+      "160.88.194.172     1\n",
+      "196.218.124.169    1\n",
+      "112.43.185.24      1\n",
+      "98.96.110.38       1\n",
+      "111.204.103.106    1\n",
+      "62.112.149.214     1\n",
+      "12.192.2.112       1\n",
+      "136.159.186.239    1\n",
+      "32.26.31.49        1\n",
+      "47.125.34.52       1\n",
+      "110.80.185.102     1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Top Destination IPs\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"TOP DESTINATION IP ADDRESSES\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "top_dst_ips = df['Destination IP Address'].value_counts().head(20)\n",
+    "print(\"\\nTop 20 Destination IPs:\")\n",
+    "print(top_dst_ips)\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.barh(range(len(top_dst_ips)), top_dst_ips.values, color='coral')\n",
+    "plt.yticks(range(len(top_dst_ips)), top_dst_ips.index)\n",
+    "plt.xlabel('Frequency (Number of Connections)', fontsize=12)\n",
+    "plt.ylabel('Destination IP Address', fontsize=12)\n",
+    "plt.title('Top 20 Most Targeted Destination IP Addresses', fontsize=14, fontweight='bold')\n",
+    "plt.gca().invert_yaxis()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "SPOOFING INDICATOR 1: FAN-OUT PATTERN (Source → Multiple Destinations)\n",
+      "================================================================================\n",
+      "\n",
+      "Fan-out Statistics:\n",
+      "  - Mean destinations per source: 1.00\n",
+      "  - Median destinations per source: 1.00\n",
+      "  - 95th percentile threshold: 1 destinations\n",
+      "  - 99th percentile threshold: 1 destinations\n",
+      "\n",
+      "Suspicious Source IPs:\n",
+      "  - IPs above 95th percentile: 0 (0.00%)\n",
+      "  - IPs above 99th percentile: 0 (0.00%)\n",
+      "\n",
+      "Top 10 Source IPs with Highest Fan-out:\n",
+      "Source IP Address\n",
+      "1.1.45.194       1\n",
+      "1.1.51.229       1\n",
+      "1.10.201.206     1\n",
+      "1.10.74.49       1\n",
+      "1.100.115.5      1\n",
+      "1.101.138.199    1\n",
+      "1.103.126.230    1\n",
+      "1.103.203.179    1\n",
+      "1.105.131.117    1\n",
+      "1.106.4.163      1\n",
+      "Name: Destination IP Address, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SPOOFING DETECTION 1: Fan-out Analysis (Source IP → Multiple Destinations)\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"SPOOFING INDICATOR 1: FAN-OUT PATTERN (Source → Multiple Destinations)\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "# Count unique destinations per source IP\n",
+    "src_to_dst_mapping = df.groupby('Source IP Address')['Destination IP Address'].nunique()\n",
+    "src_to_dst_mapping = src_to_dst_mapping.sort_values(ascending=False)\n",
+    "\n",
+    "# Calculate thresholds\n",
+    "threshold_95 = src_to_dst_mapping.quantile(0.95)\n",
+    "threshold_99 = src_to_dst_mapping.quantile(0.99)\n",
+    "\n",
+    "suspicious_sources_95 = src_to_dst_mapping[src_to_dst_mapping > threshold_95]\n",
+    "suspicious_sources_99 = src_to_dst_mapping[src_to_dst_mapping > threshold_99]\n",
+    "\n",
+    "print(f\"\\nFan-out Statistics:\")\n",
+    "print(f\"  - Mean destinations per source: {src_to_dst_mapping.mean():.2f}\")\n",
+    "print(f\"  - Median destinations per source: {src_to_dst_mapping.median():.2f}\")\n",
+    "print(f\"  - 95th percentile threshold: {threshold_95:.0f} destinations\")\n",
+    "print(f\"  - 99th percentile threshold: {threshold_99:.0f} destinations\")\n",
+    "print(f\"\\nSuspicious Source IPs:\")\n",
+    "print(f\"  - IPs above 95th percentile: {len(suspicious_sources_95)} ({len(suspicious_sources_95)/len(src_to_dst_mapping)*100:.2f}%)\")\n",
+    "print(f\"  - IPs above 99th percentile: {len(suspicious_sources_99)} ({len(suspicious_sources_99)/len(src_to_dst_mapping)*100:.2f}%)\")\n",
+    "\n",
+    "print(f\"\\nTop 10 Source IPs with Highest Fan-out:\")\n",
+    "print(src_to_dst_mapping.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize fan-out distribution\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "\n",
+    "# Histogram\n",
+    "axes[0].hist(src_to_dst_mapping.values, bins=50, color='red', alpha=0.7, edgecolor='black')\n",
+    "axes[0].axvline(threshold_95, color='darkred', linestyle='--', linewidth=2, \n",
+    "                label=f'95th percentile: {threshold_95:.0f}')\n",
+    "axes[0].axvline(threshold_99, color='maroon', linestyle='--', linewidth=2, \n",
+    "                label=f'99th percentile: {threshold_99:.0f}')\n",
+    "axes[0].set_xlabel('Number of Unique Destinations per Source IP', fontsize=12)\n",
+    "axes[0].set_ylabel('Frequency (log scale)', fontsize=12)\n",
+    "axes[0].set_title('Source IP Fan-out Distribution\\n(Potential Scanning/Spoofing)', \n",
+    "                  fontsize=14, fontweight='bold')\n",
+    "axes[0].set_yscale('log')\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Top suspicious IPs\n",
+    "top_suspicious = src_to_dst_mapping.head(15)\n",
+    "axes[1].barh(range(len(top_suspicious)), top_suspicious.values, color='darkred')\n",
+    "axes[1].set_yticks(range(len(top_suspicious)))\n",
+    "axes[1].set_yticklabels(top_suspicious.index)\n",
+    "axes[1].set_xlabel('Number of Unique Destinations', fontsize=12)\n",
+    "axes[1].set_ylabel('Source IP Address', fontsize=12)\n",
+    "axes[1].set_title('Top 15 Source IPs by Fan-out\\n(Most Suspicious)', \n",
+    "                  fontsize=14, fontweight='bold')\n",
+    "axes[1].invert_yaxis()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "SPOOFING INDICATOR 2: FAN-IN PATTERN (Multiple Sources → Destination)\n",
+      "================================================================================\n",
+      "\n",
+      "Fan-in Statistics:\n",
+      "  - Mean sources per destination: 1.00\n",
+      "  - Median sources per destination: 1.00\n",
+      "  - 95th percentile threshold: 1 sources\n",
+      "  - 99th percentile threshold: 1 sources\n",
+      "\n",
+      "Suspicious Target IPs (Potential DDoS Victims):\n",
+      "  - IPs above 95th percentile: 0 (0.00%)\n",
+      "  - IPs above 99th percentile: 0 (0.00%)\n",
+      "\n",
+      "Top 10 Target IPs with Highest Fan-in:\n",
+      "Destination IP Address\n",
+      "1.1.189.171      1\n",
+      "1.100.166.191    1\n",
+      "1.100.73.63      1\n",
+      "1.101.117.52     1\n",
+      "1.101.24.101     1\n",
+      "1.102.176.87     1\n",
+      "1.103.183.132    1\n",
+      "1.105.159.177    1\n",
+      "1.105.2.246      1\n",
+      "1.107.48.44      1\n",
+      "Name: Source IP Address, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SPOOFING DETECTION 2: Fan-in Analysis (Multiple Sources → Single Destination)\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"SPOOFING INDICATOR 2: FAN-IN PATTERN (Multiple Sources → Destination)\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "# Count unique sources per destination IP\n",
+    "dst_to_src_mapping = df.groupby('Destination IP Address')['Source IP Address'].nunique()\n",
+    "dst_to_src_mapping = dst_to_src_mapping.sort_values(ascending=False)\n",
+    "\n",
+    "# Calculate thresholds\n",
+    "threshold_95_dst = dst_to_src_mapping.quantile(0.95)\n",
+    "threshold_99_dst = dst_to_src_mapping.quantile(0.99)\n",
+    "\n",
+    "suspicious_targets_95 = dst_to_src_mapping[dst_to_src_mapping > threshold_95_dst]\n",
+    "suspicious_targets_99 = dst_to_src_mapping[dst_to_src_mapping > threshold_99_dst]\n",
+    "\n",
+    "print(f\"\\nFan-in Statistics:\")\n",
+    "print(f\"  - Mean sources per destination: {dst_to_src_mapping.mean():.2f}\")\n",
+    "print(f\"  - Median sources per destination: {dst_to_src_mapping.median():.2f}\")\n",
+    "print(f\"  - 95th percentile threshold: {threshold_95_dst:.0f} sources\")\n",
+    "print(f\"  - 99th percentile threshold: {threshold_99_dst:.0f} sources\")\n",
+    "print(f\"\\nSuspicious Target IPs (Potential DDoS Victims):\")\n",
+    "print(f\"  - IPs above 95th percentile: {len(suspicious_targets_95)} ({len(suspicious_targets_95)/len(dst_to_src_mapping)*100:.2f}%)\")\n",
+    "print(f\"  - IPs above 99th percentile: {len(suspicious_targets_99)} ({len(suspicious_targets_99)/len(dst_to_src_mapping)*100:.2f}%)\")\n",
+    "\n",
+    "print(f\"\\nTop 10 Target IPs with Highest Fan-in:\")\n",
+    "print(dst_to_src_mapping.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize fan-in distribution\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "\n",
+    "# Histogram\n",
+    "axes[0].hist(dst_to_src_mapping.values, bins=50, color='purple', alpha=0.7, edgecolor='black')\n",
+    "axes[0].axvline(threshold_95_dst, color='darkviolet', linestyle='--', linewidth=2, \n",
+    "                label=f'95th percentile: {threshold_95_dst:.0f}')\n",
+    "axes[0].axvline(threshold_99_dst, color='indigo', linestyle='--', linewidth=2, \n",
+    "                label=f'99th percentile: {threshold_99_dst:.0f}')\n",
+    "axes[0].set_xlabel('Number of Unique Sources per Destination IP', fontsize=12)\n",
+    "axes[0].set_ylabel('Frequency (log scale)', fontsize=12)\n",
+    "axes[0].set_title('Destination IP Fan-in Distribution\\n(Potential DDoS Targets)', \n",
+    "                  fontsize=14, fontweight='bold')\n",
+    "axes[0].set_yscale('log')\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Top targeted IPs\n",
+    "top_targets = dst_to_src_mapping.head(15)\n",
+    "axes[1].barh(range(len(top_targets)), top_targets.values, color='darkviolet')\n",
+    "axes[1].set_yticks(range(len(top_targets)))\n",
+    "axes[1].set_yticklabels(top_targets.index)\n",
+    "axes[1].set_xlabel('Number of Unique Sources', fontsize=12)\n",
+    "axes[1].set_ylabel('Destination IP Address', fontsize=12)\n",
+    "axes[1].set_title('Top 15 Destination IPs by Fan-in\\n(Potential DDoS Targets)', \n",
+    "                  fontsize=14, fontweight='bold')\n",
+    "axes[1].invert_yaxis()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "SPOOFING INDICATOR 3: BIDIRECTIONAL TRAFFIC\n",
+      "================================================================================\n",
+      "\n",
+      "Bidirectional IP Statistics:\n",
+      "  - Total unique source IPs: 40,000\n",
+      "  - Total unique destination IPs: 40,000\n",
+      "  - IPs appearing as BOTH source and destination: 1\n",
+      "  - Percentage of bidirectional IPs: 0.00%\n",
+      "\n",
+      "Bidirectional Traffic by Attack Type:\n",
+      "is_bidirectional       False     True \n",
+      "Attack Type                           \n",
+      "DDoS               99.992553  0.007447\n",
+      "Intrusion          99.992461  0.007539\n",
+      "Malware           100.000000  0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SPOOFING DETECTION 3: Bidirectional Traffic Analysis\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"SPOOFING INDICATOR 3: BIDIRECTIONAL TRAFFIC\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "source_ips_set = set(df['Source IP Address'].dropna())\n",
+    "dest_ips_set = set(df['Destination IP Address'].dropna())\n",
+    "bidirectional_ips = source_ips_set.intersection(dest_ips_set)\n",
+    "\n",
+    "print(f\"\\nBidirectional IP Statistics:\")\n",
+    "print(f\"  - Total unique source IPs: {len(source_ips_set):,}\")\n",
+    "print(f\"  - Total unique destination IPs: {len(dest_ips_set):,}\")\n",
+    "print(f\"  - IPs appearing as BOTH source and destination: {len(bidirectional_ips):,}\")\n",
+    "print(f\"  - Percentage of bidirectional IPs: {len(bidirectional_ips)/(len(source_ips_set.union(dest_ips_set)))*100:.2f}%\")\n",
+    "\n",
+    "# Analyze bidirectional traffic by attack type\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    df['is_bidirectional'] = (df['Source IP Address'].isin(bidirectional_ips)) | \\\n",
+    "                              (df['Destination IP Address'].isin(bidirectional_ips))\n",
+    "    \n",
+    "    print(f\"\\nBidirectional Traffic by Attack Type:\")\n",
+    "    bidir_attack = pd.crosstab(df['Attack Type'], df['is_bidirectional'], normalize='index') * 100\n",
+    "    print(bidir_attack)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Timestamp",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Source IP Address",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Destination IP Address",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Source Port",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Destination Port",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Protocol",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Packet Length",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Packet Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Traffic Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Payload Data",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Malware Indicators",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Anomaly Scores",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Alerts/Warnings",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Attack Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Attack Signature",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Action Taken",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Severity Level",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "User Information",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Device Information",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Network Segment",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Geo-location Data",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Proxy Information",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Firewall Logs",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "IDS/IPS Alerts",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Log Source",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "has_proxy",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "is_bidirectional",
+         "rawType": "bool",
+         "type": "boolean"
+        },
+        {
+         "name": "src_is_private",
+         "rawType": "bool",
+         "type": "boolean"
+        },
+        {
+         "name": "dst_is_private",
+         "rawType": "bool",
+         "type": "boolean"
+        },
+        {
+         "name": "packet_length_bin",
+         "rawType": "category",
+         "type": "unknown"
+        },
+        {
+         "name": "dst_port_category",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "src_port_category",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "anomaly_category",
+         "rawType": "category",
+         "type": "unknown"
+        },
+        {
+         "name": "src_ip_class",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "dst_ip_class",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "4101c96f-5ee0-4624-93f6-7e186633f273",
+       "rows": [
+        [
+         "0",
+         "2023-05-30 06:33:58",
+         "103.216.15.12",
+         "84.9.164.252",
+         "31225",
+         "17616",
+         "ICMP",
+         "503",
+         "Data",
+         "HTTP",
+         "Qui natus odio asperiores nam. Optio nobis iusto accusamus ad perferendis esse at. Asperiores neque at ad.\nMaiores possimus ipsum saepe vitae. Ad possimus veritatis.",
+         "IoC Detected",
+         "28.67",
+         null,
+         "Malware",
+         "Known Pattern B",
+         "Logged",
+         "Low",
+         "Reyansh Dugal",
+         "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.2; Trident/5.0)",
+         "Segment A",
+         "Jamshedpur, Sikkim",
+         "150.9.97.135",
+         "Log Data",
+         null,
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "103",
+         "84"
+        ],
+        [
+         "1",
+         "2020-08-26 07:08:30",
+         "78.199.217.198",
+         "66.191.137.154",
+         "17245",
+         "48166",
+         "ICMP",
+         "1174",
+         "Data",
+         "HTTP",
+         "Aperiam quos modi officiis veritatis rem. Omnis nulla dolore perspiciatis.\nIllo animi mollitia vero voluptates error ad. Quidem maxime eaque optio a. Consectetur quasi veniam et totam culpa ullam.",
+         "IoC Detected",
+         "51.5",
+         null,
+         "Malware",
+         "Known Pattern A",
+         "Blocked",
+         "Low",
+         "Sumer Rana",
+         "Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)",
+         "Segment B",
+         "Bilaspur, Nagaland",
+         null,
+         "Log Data",
+         null,
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "78",
+         "66"
+        ],
+        [
+         "2",
+         "2022-11-13 08:23:25",
+         "63.79.210.48",
+         "198.219.82.17",
+         "16811",
+         "53600",
+         "UDP",
+         "306",
+         "Control",
+         "HTTP",
+         "Perferendis sapiente vitae soluta. Hic delectus quae nemo ea esse est rerum.",
+         "IoC Detected",
+         "87.42",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "Low",
+         "Himmat Karpe",
+         "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.2; Trident/5.0)",
+         "Segment C",
+         "Bokaro, Rajasthan",
+         "114.133.48.179",
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Dynamic (49152-65535)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "63",
+         "198"
+        ],
+        [
+         "3",
+         "2023-07-02 10:38:46",
+         "163.42.196.10",
+         "101.228.192.255",
+         "20018",
+         "32534",
+         "UDP",
+         "385",
+         "Data",
+         "HTTP",
+         "Totam maxime beatae expedita explicabo porro labore. Minima ab fugit officiis dicta perspiciatis pariatur. Facilis voluptates eligendi dolores eveniet deserunt. Eveniet reprehenderit culpa quo.",
+         null,
+         "15.79",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Blocked",
+         "Medium",
+         "Fateh Kibe",
+         "Mozilla/5.0 (Macintosh; PPC Mac OS X 10_11_5; rv:1.9.6.20) Gecko/2583-02-14 13:30:10 Firefox/11.0",
+         "Segment B",
+         "Jaunpur, Rajasthan",
+         null,
+         null,
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Low (0-25%)",
+         "163",
+         "101"
+        ],
+        [
+         "4",
+         "2023-07-16 13:11:07",
+         "71.166.185.76",
+         "189.243.174.238",
+         "6131",
+         "26646",
+         "TCP",
+         "1462",
+         "Data",
+         "DNS",
+         "Odit nesciunt dolorem nisi iste iusto. Animi voluptates soluta quis doloribus quas. Iure harum nihil hic illo repellendus.\nQuia illo fugit eligendi doloremque. In doloremque autem iure.",
+         null,
+         "0.52",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Blocked",
+         "Low",
+         "Dhanush Chad",
+         "Mozilla/5.0 (compatible; MSIE 5.0; Windows NT 6.2; Trident/3.0)",
+         "Segment C",
+         "Anantapur, Tripura",
+         "149.6.110.119",
+         null,
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Low (0-25%)",
+         "71",
+         "189"
+        ],
+        [
+         "5",
+         "2022-10-28 13:14:27",
+         "198.102.5.160",
+         "147.190.155.133",
+         "17430",
+         "52805",
+         "UDP",
+         "1423",
+         "Data",
+         "HTTP",
+         "Repellat quas illum harum fugit incidunt exercitationem illum. Voluptate asperiores aperiam magnam eius. Eos quis repellat eos.",
+         null,
+         "5.76",
+         null,
+         "Malware",
+         "Known Pattern A",
+         "Logged",
+         "Medium",
+         "Zeeshan Viswanathan",
+         "Opera/8.58.(X11; Linux i686; nl-NL) Presto/2.9.170 Version/12.00",
+         "Segment C",
+         "Aurangabad, Meghalaya",
+         null,
+         null,
+         null,
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Dynamic (49152-65535)",
+         "Registered (1024-49151)",
+         "Low (0-25%)",
+         "198",
+         "147"
+        ],
+        [
+         "6",
+         "2022-05-16 17:55:43",
+         "97.253.103.59",
+         "77.16.101.53",
+         "26562",
+         "17416",
+         "TCP",
+         "379",
+         "Data",
+         "DNS",
+         "Qui numquam inventore repellat ratione fugit odit. Quidem est possimus voluptates reprehenderit vitae a. Quibusdam in itaque rerum.\nExcepturi quisquam iusto provident adipisci.",
+         null,
+         "31.55",
+         null,
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "High",
+         "Ehsaan Dalal",
+         "Opera/9.24.(X11; Linux i686; fa-IR) Presto/2.9.175 Version/10.00",
+         "Segment A",
+         "Eluru, Manipur",
+         null,
+         "Log Data",
+         null,
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "97",
+         "77"
+        ],
+        [
+         "7",
+         "2023-02-12 07:13:17",
+         "11.48.99.245",
+         "178.157.14.116",
+         "34489",
+         "20396",
+         "ICMP",
+         "1022",
+         "Data",
+         "DNS",
+         "Amet libero optio quidem praesentium libero. Ea magnam atque corporis ipsum iure iusto.\nEveniet dolor odio libero. Minus iste fugit asperiores minima eos ipsum.",
+         "IoC Detected",
+         "54.05",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern A",
+         "Logged",
+         "High",
+         "Yuvaan Dubey",
+         "Mozilla/5.0 (Macintosh; U; PPC Mac OS X 10_7_6) AppleWebKit/534.2 (KHTML, like Gecko) Chrome/45.0.865.0 Safari/534.2",
+         "Segment A",
+         "Phagwara, Andhra Pradesh",
+         "192.31.159.5",
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "11",
+         "178"
+        ],
+        [
+         "8",
+         "2023-06-27 11:02:56",
+         "49.32.208.167",
+         "72.202.237.9",
+         "56296",
+         "20857",
+         "TCP",
+         "1281",
+         "Control",
+         "FTP",
+         "Veritatis nihil amet atque molestias aperiam minus. Velit hic aperiam iusto vitae nulla dolor maxime. Atque aspernatur reiciendis in.",
+         "IoC Detected",
+         "56.34",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern A",
+         "Blocked",
+         "High",
+         "Zaina Iyer",
+         "Mozilla/5.0 (Macintosh; U; PPC Mac OS X 10_5_8) AppleWebKit/536.1 (KHTML, like Gecko) Chrome/38.0.861.0 Safari/536.1",
+         "Segment B",
+         "Ambala, Tripura",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "High (50-75%)",
+         "49",
+         "72"
+        ],
+        [
+         "9",
+         "2021-08-15 22:29:04",
+         "114.109.149.113",
+         "160.88.194.172",
+         "37918",
+         "50039",
+         "UDP",
+         "224",
+         "Data",
+         "HTTP",
+         "Consequatur ipsum autem reprehenderit quae. Doloribus dicta laboriosam porro consequatur dicta deleniti. Hic doloribus non aliquam.",
+         null,
+         "16.51",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Blocked",
+         "Medium",
+         "Mishti Chaudhuri",
+         "Mozilla/5.0 (Windows; U; Windows NT 6.0) AppleWebKit/533.28.5 (KHTML, like Gecko) Version/4.0 Safari/533.28.5",
+         "Segment A",
+         "Rampur, Mizoram",
+         "87.128.245.244",
+         null,
+         null,
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "128-256",
+         "Dynamic (49152-65535)",
+         "Registered (1024-49151)",
+         "Low (0-25%)",
+         "114",
+         "160"
+        ],
+        [
+         "10",
+         "2022-07-20 13:28:50",
+         "177.21.83.200",
+         "196.218.124.169",
+         "35538",
+         "35006",
+         "ICMP",
+         "661",
+         "Data",
+         "HTTP",
+         "Sequi maxime voluptate ea. Eius officiis eaque. Nesciunt aspernatur rerum qui aperiam earum.\nEius dolore molestias. Voluptas laboriosam vero suscipit assumenda inventore.",
+         null,
+         "24.91",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Ignored",
+         "Medium",
+         "Hunar Sem",
+         "Mozilla/5.0 (iPod; U; CPU iPhone OS 4_3 like Mac OS X; xh-ZA) AppleWebKit/534.25.1 (KHTML, like Gecko) Version/4.0.5 Mobile/8B111 Safari/6534.25.1",
+         "Segment B",
+         "Gangtok, Haryana",
+         "29.161.99.247",
+         null,
+         null,
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Low (0-25%)",
+         "177",
+         "196"
+        ],
+        [
+         "11",
+         "2022-06-26 15:15:50",
+         "92.4.25.171",
+         "112.43.185.24",
+         "10903",
+         "36817",
+         "TCP",
+         "281",
+         "Control",
+         "HTTP",
+         "Nihil praesentium asperiores omnis ullam libero. Facilis eius aspernatur perspiciatis inventore veniam. Laborum nobis ex iste.",
+         null,
+         "86.07",
+         null,
+         "Malware",
+         "Known Pattern B",
+         "Ignored",
+         "Low",
+         "Mehul Raj",
+         "Opera/8.38.(X11; Linux x86_64; pt-BR) Presto/2.9.175 Version/12.00",
+         "Segment B",
+         "Nandyal, Mizoram",
+         null,
+         null,
+         null,
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "92",
+         "112"
+        ],
+        [
+         "12",
+         "2020-09-30 21:35:31",
+         "57.91.207.84",
+         "98.96.110.38",
+         "53471",
+         "38048",
+         "ICMP",
+         "64",
+         "Control",
+         "DNS",
+         "Earum sit est et eaque ipsam. Vero repellendus error iusto eveniet.",
+         null,
+         "74.2",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern A",
+         "Blocked",
+         "Medium",
+         "Vaibhav Kala",
+         "Opera/8.54.(Windows NT 6.0; tg-TJ) Presto/2.9.170 Version/12.00",
+         "Segment B",
+         "Silchar, Kerala",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "0-64",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "High (50-75%)",
+         "57",
+         "98"
+        ],
+        [
+         "13",
+         "2021-01-13 02:30:18",
+         "80.28.21.123",
+         "111.204.103.106",
+         "43729",
+         "10845",
+         "ICMP",
+         "1341",
+         "Data",
+         "HTTP",
+         "Ab voluptatibus nam aperiam. Est sed nostrum animi quae quas. Rerum minus possimus quis.\nCupiditate provident doloremque aliquam maiores tenetur tempora.\nSimilique dolor ullam illo placeat.",
+         null,
+         "62.14",
+         null,
+         "Malware",
+         "Known Pattern A",
+         "Ignored",
+         "Low",
+         "Siya Gera",
+         "Mozilla/5.0 (compatible; MSIE 7.0; Windows NT 10.0; Trident/4.1)",
+         "Segment C",
+         "Gudivada, Odisha",
+         "59.131.15.72",
+         null,
+         null,
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "80",
+         "111"
+        ],
+        [
+         "14",
+         "2023-02-01 13:17:17",
+         "54.163.130.178",
+         "62.112.149.214",
+         "47795",
+         "35243",
+         "UDP",
+         "913",
+         "Data",
+         "DNS",
+         "Sit doloremque explicabo. Cupiditate placeat exercitationem. Dolores rerum iste necessitatibus libero voluptate.\nSuscipit itaque inventore eveniet ipsa. Ea ipsa minima impedit aliquam cumque.",
+         null,
+         "72.65",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Logged",
+         "High",
+         "Inaaya  Soman",
+         "Mozilla/5.0 (X11; Linux i686; rv:1.9.7.20) Gecko/5645-10-22 09:50:59 Firefox/3.8",
+         "Segment B",
+         "Silchar, Maharashtra",
+         "155.121.88.187",
+         null,
+         null,
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "54",
+         "62"
+        ],
+        [
+         "15",
+         "2023-09-15 20:35:21",
+         "130.163.192.252",
+         "12.192.2.112",
+         "24912",
+         "21176",
+         "TCP",
+         "425",
+         "Data",
+         "DNS",
+         "Voluptates nam doloremque. Eius amet officiis aperiam delectus mollitia pariatur. Ducimus aut in dolorem ipsum molestiae.",
+         null,
+         "91.56",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern B",
+         "Logged",
+         "Medium",
+         "Shaan Subramaniam",
+         "Opera/8.85.(X11; Linux x86_64; sk-SK) Presto/2.9.163 Version/11.00",
+         "Segment C",
+         "Bhiwandi, Tripura",
+         null,
+         "Log Data",
+         null,
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "130",
+         "12"
+        ],
+        [
+         "16",
+         "2023-04-18 16:46:46",
+         "4.255.187.165",
+         "136.159.186.239",
+         "39934",
+         "5259",
+         "TCP",
+         "838",
+         "Control",
+         "HTTP",
+         "Sed facere culpa aliquam. Adipisci error quisquam reprehenderit itaque fugit illum. Minima exercitationem occaecati nemo repudiandae officia quasi. Tempora maxime commodi illo.",
+         null,
+         "43.83",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "High",
+         "Samarth Ratti",
+         "Opera/8.72.(Windows NT 6.1; ia-FR) Presto/2.9.160 Version/12.00",
+         "Segment B",
+         "Solapur, Telangana",
+         null,
+         "Log Data",
+         null,
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "4",
+         "136"
+        ],
+        [
+         "17",
+         "2023-02-21 07:02:55",
+         "212.164.196.41",
+         "32.26.31.49",
+         "16513",
+         "50583",
+         "TCP",
+         "969",
+         "Data",
+         "HTTP",
+         "Dolore nisi voluptatem. Debitis explicabo fuga perferendis esse omnis.\nSit voluptatibus aliquid eos molestias cumque. Harum rem doloremque officiis nesciunt velit suscipit.",
+         null,
+         "40.46",
+         null,
+         "DDoS",
+         "Known Pattern A",
+         "Logged",
+         "Low",
+         "Shayak Kapadia",
+         "Mozilla/5.0 (X11; Linux i686) AppleWebKit/536.0 (KHTML, like Gecko) Chrome/46.0.873.0 Safari/536.0",
+         "Segment B",
+         "Darbhanga, Mizoram",
+         "20.252.145.34",
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Dynamic (49152-65535)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "212",
+         "32"
+        ],
+        [
+         "18",
+         "2020-12-29 02:50:47",
+         "119.101.120.119",
+         "47.125.34.52",
+         "4510",
+         "26783",
+         "TCP",
+         "124",
+         "Control",
+         "DNS",
+         "Qui sapiente laboriosam minima veritatis impedit vel. Placeat itaque incidunt non. Consequuntur quo quod.\nRecusandae similique explicabo error labore impedit et. Beatae tenetur voluptatum veniam.",
+         null,
+         "44.75",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "High",
+         "Faiyaz Sathe",
+         "Mozilla/5.0 (compatible; MSIE 6.0; Windows NT 10.0; Trident/4.0)",
+         "Segment C",
+         "Rampur, Rajasthan",
+         null,
+         null,
+         null,
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "64-128",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "119",
+         "47"
+        ],
+        [
+         "19",
+         "2020-07-27 00:00:53",
+         "104.176.150.78",
+         "110.80.185.102",
+         "55147",
+         "17595",
+         "TCP",
+         "461",
+         "Data",
+         "HTTP",
+         "Ipsa est placeat cumque ut id. Quisquam provident quidem beatae. Corporis nam aut expedita reprehenderit.\nExpedita eum cupiditate dolor beatae inventore minus.",
+         "IoC Detected",
+         "13.19",
+         null,
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "High",
+         "Zara Sachar",
+         "Mozilla/5.0 (iPhone; CPU iPhone OS 12_4_8 like Mac OS X) AppleWebKit/533.2 (KHTML, like Gecko) FxiOS/18.1i4735.0 Mobile/92M384 Safari/533.2",
+         "Segment B",
+         "Ahmednagar, Uttarakhand",
+         "106.203.136.99",
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "Low (0-25%)",
+         "104",
+         "110"
+        ],
+        [
+         "20",
+         "2021-07-16 20:45:42",
+         "116.61.253.182",
+         "170.92.148.90",
+         "24907",
+         "37634",
+         "ICMP",
+         "845",
+         "Control",
+         "DNS",
+         "Ipsam ullam doloribus magni eos beatae eveniet. Labore provident vel perspiciatis doloribus excepturi.\nModi eaque dolor ut.",
+         null,
+         "89.99",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Blocked",
+         "Medium",
+         "Manjari Randhawa",
+         "Mozilla/5.0 (Macintosh; U; PPC Mac OS X 10_11_1 rv:4.0; an-ES) AppleWebKit/533.41.7 (KHTML, like Gecko) Version/4.0.4 Safari/533.41.7",
+         "Segment A",
+         "Kharagpur, West Bengal",
+         "71.135.96.151",
+         "Log Data",
+         null,
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "116",
+         "170"
+        ],
+        [
+         "21",
+         "2022-04-29 14:46:46",
+         "214.247.90.42",
+         "196.26.121.164",
+         "37998",
+         "1757",
+         "UDP",
+         "410",
+         "Control",
+         "DNS",
+         "Architecto voluptatum delectus.\nAut quia fugiat molestias eum magnam animi. Dignissimos incidunt magnam officiis omnis iste soluta.",
+         "IoC Detected",
+         "86.91",
+         null,
+         "Malware",
+         "Known Pattern B",
+         "Blocked",
+         "Low",
+         "Sahil Kapoor",
+         "Mozilla/5.0 (Windows; U; Windows NT 5.01) AppleWebKit/531.13.4 (KHTML, like Gecko) Version/4.0 Safari/531.13.4",
+         "Segment C",
+         "Tadepalligudem, Rajasthan",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "214",
+         "196"
+        ],
+        [
+         "22",
+         "2023-02-24 06:39:25",
+         "57.7.171.107",
+         "76.146.23.52",
+         "3133",
+         "46077",
+         "UDP",
+         "1425",
+         "Data",
+         "DNS",
+         "Praesentium voluptatibus sapiente.",
+         null,
+         "72.25",
+         null,
+         "DDoS",
+         "Known Pattern A",
+         "Logged",
+         "High",
+         "Akarsh Khurana",
+         "Mozilla/5.0 (compatible; MSIE 5.0; Windows 95; Trident/5.1)",
+         "Segment A",
+         "Gaya, Gujarat",
+         "169.32.12.215",
+         null,
+         "Alert Data",
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "57",
+         "76"
+        ],
+        [
+         "23",
+         "2023-09-15 15:43:03",
+         "46.236.76.78",
+         "68.194.124.162",
+         "40832",
+         "28015",
+         "UDP",
+         "118",
+         "Data",
+         "HTTP",
+         "Esse porro corrupti. Iusto magnam sunt dolorem fuga explicabo.\nIncidunt corrupti rem quisquam. Est iure suscipit. Pariatur tempora suscipit cum.",
+         "IoC Detected",
+         "96.27",
+         null,
+         "Malware",
+         "Known Pattern A",
+         "Logged",
+         "Medium",
+         "Dhanush Sagar",
+         "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 5.0; Trident/5.1)",
+         "Segment A",
+         "Chandrapur, Chhattisgarh",
+         "50.108.129.83",
+         null,
+         null,
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "64-128",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "46",
+         "68"
+        ],
+        [
+         "24",
+         "2023-10-06 06:53:51",
+         "71.41.31.239",
+         "105.193.254.47",
+         "39911",
+         "27107",
+         "TCP",
+         "1386",
+         "Control",
+         "DNS",
+         "Ducimus totam dolor fugiat ipsa. Eum quas consectetur distinctio cum.\nVoluptatum numquam ab quasi molestiae laboriosam maiores. Illo nemo nulla cupiditate placeat nemo quo.",
+         "IoC Detected",
+         "67.43",
+         null,
+         "DDoS",
+         "Known Pattern A",
+         "Blocked",
+         "Medium",
+         "Anika Sharaf",
+         "Mozilla/5.0 (compatible; MSIE 5.0; Windows NT 5.1; Trident/5.1)",
+         "Segment C",
+         "Guntakal, Rajasthan",
+         null,
+         "Log Data",
+         null,
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "71",
+         "105"
+        ],
+        [
+         "25",
+         "2023-10-06 02:37:35",
+         "128.47.86.24",
+         "9.149.23.14",
+         "23021",
+         "31279",
+         "UDP",
+         "433",
+         "Control",
+         "DNS",
+         "Neque repellendus modi debitis dolorem officia. Placeat quae ipsum sapiente. Quia aspernatur dolores incidunt.",
+         null,
+         "89.86",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern A",
+         "Logged",
+         "Low",
+         "Drishya Zachariah",
+         "Mozilla/5.0 (Windows; U; Windows 98; Win 9x 4.90) AppleWebKit/535.3.1 (KHTML, like Gecko) Version/5.0.4 Safari/535.3.1",
+         "Segment C",
+         "Giridih, Assam",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "128",
+         "9"
+        ],
+        [
+         "26",
+         "2021-01-31 03:12:04",
+         "182.232.245.98",
+         "153.211.183.215",
+         "32552",
+         "15521",
+         "UDP",
+         "531",
+         "Control",
+         "FTP",
+         "Ad officia corporis laudantium enim cumque facere. Tempora eos ad sed eaque minima quisquam.\nCupiditate magni alias quisquam veniam. Quaerat nobis quos ad molestiae eligendi.",
+         null,
+         "87.23",
+         null,
+         "Malware",
+         "Known Pattern A",
+         "Blocked",
+         "High",
+         "Renee Ramachandran",
+         "Mozilla/5.0 (Windows; U; Windows NT 5.1) AppleWebKit/533.47.5 (KHTML, like Gecko) Version/5.1 Safari/533.47.5",
+         "Segment C",
+         "Katihar, Andhra Pradesh",
+         "183.242.33.16",
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "182",
+         "153"
+        ],
+        [
+         "27",
+         "2020-08-28 09:54:45",
+         "208.44.127.159",
+         "121.167.40.167",
+         "56607",
+         "33023",
+         "TCP",
+         "282",
+         "Control",
+         "HTTP",
+         "Fugit quae placeat dicta tenetur ipsa. Facilis optio corrupti nulla. Eos iste voluptatum natus dignissimos fugiat.",
+         "IoC Detected",
+         "44.4",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern A",
+         "Logged",
+         "Low",
+         "Anahi Apte",
+         "Mozilla/5.0 (compatible; MSIE 7.0; Windows NT 6.0; Trident/5.0)",
+         "Segment A",
+         "Shahjahanpur, Andhra Pradesh",
+         "180.52.47.153",
+         null,
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "Medium (25-50%)",
+         "208",
+         "121"
+        ],
+        [
+         "28",
+         "2021-05-29 15:33:51",
+         "197.137.61.248",
+         "87.49.219.65",
+         "64351",
+         "43307",
+         "ICMP",
+         "1487",
+         "Control",
+         "HTTP",
+         "Esse esse optio excepturi delectus dolore molestiae omnis. Eligendi omnis sapiente voluptatem corporis libero pariatur consequuntur.",
+         "IoC Detected",
+         "45.42",
+         null,
+         "Intrusion",
+         "Known Pattern B",
+         "Blocked",
+         "Low",
+         "Faiyaz Som",
+         "Opera/8.62.(X11; Linux i686; ru-UA) Presto/2.9.184 Version/11.00",
+         "Segment C",
+         "Solapur, Madhya Pradesh",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "Medium (25-50%)",
+         "197",
+         "87"
+        ],
+        [
+         "29",
+         "2022-05-30 18:26:12",
+         "69.60.34.24",
+         "160.208.108.125",
+         "19954",
+         "11594",
+         "UDP",
+         "563",
+         "Control",
+         "DNS",
+         "Quasi tempore quam recusandae. Voluptate earum placeat similique. Id voluptatibus voluptatem quas numquam expedita saepe. Itaque aspernatur fugit beatae nulla.",
+         "IoC Detected",
+         "25.7",
+         null,
+         "Intrusion",
+         "Known Pattern B",
+         "Ignored",
+         "Medium",
+         "Kavya Shenoy",
+         "Mozilla/5.0 (X11; Linux i686) AppleWebKit/533.0 (KHTML, like Gecko) Chrome/61.0.892.0 Safari/533.0",
+         "Segment C",
+         "Coimbatore, Telangana",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "69",
+         "160"
+        ],
+        [
+         "30",
+         "2022-08-02 19:15:37",
+         "110.207.118.63",
+         "172.36.150.79",
+         "63062",
+         "49527",
+         "TCP",
+         "1313",
+         "Control",
+         "HTTP",
+         "Debitis quam dolor incidunt esse quaerat amet. Ex voluptatibus temporibus beatae nulla rem rerum quam. Accusantium quibusdam velit sit.",
+         "IoC Detected",
+         "69.2",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Logged",
+         "Low",
+         "Tushar Borra",
+         "Mozilla/5.0 (iPad; CPU iPad OS 3_1_3 like Mac OS X) AppleWebKit/536.0 (KHTML, like Gecko) CriOS/61.0.810.0 Mobile/41V682 Safari/536.0",
+         "Segment C",
+         "Serampore, West Bengal",
+         null,
+         "Log Data",
+         null,
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Dynamic (49152-65535)",
+         "Dynamic (49152-65535)",
+         "High (50-75%)",
+         "110",
+         "172"
+        ],
+        [
+         "31",
+         "2023-01-10 12:29:37",
+         "188.40.103.145",
+         "84.139.233.32",
+         "39208",
+         "61136",
+         "UDP",
+         "350",
+         "Control",
+         "HTTP",
+         "Ipsa dolore eligendi amet necessitatibus. Voluptas neque praesentium possimus magni vero nam. Eius quas reiciendis rem iure tempore dolore. Asperiores ducimus omnis tenetur consequatur iure sequi.",
+         null,
+         "77.06",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern A",
+         "Ignored",
+         "Medium",
+         "Mannat Goyal",
+         "Mozilla/5.0 (Windows; U; Windows NT 5.0) AppleWebKit/535.44.6 (KHTML, like Gecko) Version/5.0 Safari/535.44.6",
+         "Segment A",
+         "Gudivada, Karnataka",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Dynamic (49152-65535)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "188",
+         "84"
+        ],
+        [
+         "32",
+         "2020-10-15 19:05:37",
+         "99.8.190.190",
+         "91.166.229.253",
+         "12932",
+         "47900",
+         "TCP",
+         "373",
+         "Data",
+         "DNS",
+         "Iste harum voluptas hic harum sapiente suscipit sit. Est atque veniam aliquid ipsam aut. Eveniet pariatur id facilis.\nQuidem quibusdam dolor. Incidunt qui illo repellat vitae.",
+         "IoC Detected",
+         "3.87",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern A",
+         "Ignored",
+         "Medium",
+         "Tushar Shenoy",
+         "Opera/9.76.(X11; Linux i686; ha-NG) Presto/2.9.181 Version/12.00",
+         "Segment B",
+         "Nellore, Tripura",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Low (0-25%)",
+         "99",
+         "91"
+        ],
+        [
+         "33",
+         "2021-06-09 18:55:30",
+         "128.62.6.140",
+         "4.104.218.143",
+         "11482",
+         "16459",
+         "ICMP",
+         "1155",
+         "Control",
+         "DNS",
+         "Maxime nihil illo recusandae iusto minima. Fugiat occaecati ullam tempora.\nEarum nulla laudantium illum rem voluptatem libero. Nesciunt aliquam officiis accusantium sunt laboriosam repudiandae.",
+         null,
+         "25.93",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern A",
+         "Blocked",
+         "Low",
+         "Jiya Devan",
+         "Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_12_0) AppleWebKit/533.2 (KHTML, like Gecko) Chrome/45.0.897.0 Safari/533.2",
+         "Segment B",
+         "Ludhiana, Goa",
+         "40.171.118.90",
+         null,
+         null,
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "128",
+         "4"
+        ],
+        [
+         "34",
+         "2020-07-08 08:08:54",
+         "211.43.37.93",
+         "93.146.128.133",
+         "18768",
+         "8138",
+         "TCP",
+         "521",
+         "Data",
+         "FTP",
+         "Dignissimos a rerum. Corrupti distinctio odio eligendi culpa optio.\nA quis odit error. Quia ipsum consequatur mollitia animi nihil consequuntur. Sapiente reprehenderit placeat modi quaerat.",
+         "IoC Detected",
+         "60.57",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "High",
+         "Yasmin Balan",
+         "Mozilla/5.0 (iPhone; CPU iPhone OS 7_1_2 like Mac OS X) AppleWebKit/532.2 (KHTML, like Gecko) FxiOS/10.1v4161.0 Mobile/63G518 Safari/532.2",
+         "Segment C",
+         "Orai, Tamil Nadu",
+         null,
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "211",
+         "93"
+        ],
+        [
+         "35",
+         "2023-09-23 19:07:33",
+         "203.171.62.228",
+         "27.5.94.221",
+         "41615",
+         "15184",
+         "ICMP",
+         "1346",
+         "Data",
+         "FTP",
+         "Magni blanditiis veritatis asperiores nihil. Similique est quibusdam cupiditate. Voluptas a dolorem. Explicabo sapiente autem.",
+         "IoC Detected",
+         "67.73",
+         null,
+         "Intrusion",
+         "Known Pattern A",
+         "Blocked",
+         "Low",
+         "Bhavin Chaudhari",
+         "Mozilla/5.0 (Android 7.1; Mobile; rv:22.0) Gecko/22.0 Firefox/22.0",
+         "Segment A",
+         "Jaunpur, Uttar Pradesh",
+         null,
+         null,
+         "Alert Data",
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "High (50-75%)",
+         "203",
+         "27"
+        ],
+        [
+         "36",
+         "2022-05-22 03:10:27",
+         "115.120.220.179",
+         "72.56.150.150",
+         "34512",
+         "40467",
+         "UDP",
+         "1311",
+         "Control",
+         "DNS",
+         "Id repellat nesciunt atque cumque ullam.\nQuasi sunt autem enim totam temporibus. Nemo pariatur nemo possimus ducimus odio sunt impedit.\nBeatae molestias dolorum vitae.",
+         "IoC Detected",
+         "75.16",
+         null,
+         "DDoS",
+         "Known Pattern B",
+         "Blocked",
+         "Medium",
+         "Aniruddh Dani",
+         "Mozilla/5.0 (iPhone; CPU iPhone OS 5_1_1 like Mac OS X) AppleWebKit/534.2 (KHTML, like Gecko) FxiOS/16.4h8714.0 Mobile/30G353 Safari/534.2",
+         "Segment C",
+         "Hubli–Dharwad, Chhattisgarh",
+         null,
+         null,
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "115",
+         "72"
+        ],
+        [
+         "37",
+         "2022-06-04 20:45:18",
+         "140.29.40.43",
+         "61.57.62.247",
+         "28975",
+         "5960",
+         "UDP",
+         "117",
+         "Control",
+         "FTP",
+         "Mollitia beatae esse saepe culpa. Deserunt ipsa incidunt ipsam mollitia.\nIusto ipsum cupiditate ipsa sed. Maiores minima vero. Iste dolores qui ea neque maiores.",
+         null,
+         "75.46",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Logged",
+         "Low",
+         "Eva Dalal",
+         "Mozilla/5.0 (Android 1.0; Mobile; rv:45.0) Gecko/45.0 Firefox/45.0",
+         "Segment B",
+         "Sagar, Mizoram",
+         null,
+         null,
+         null,
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "64-128",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "140",
+         "61"
+        ],
+        [
+         "38",
+         "2022-12-29 23:42:12",
+         "76.128.95.185",
+         "142.92.111.242",
+         "46111",
+         "15554",
+         "TCP",
+         "1271",
+         "Control",
+         "HTTP",
+         "Ad minima praesentium ducimus doloribus nobis quos. Mollitia illum laudantium vitae enim.\nOptio et laboriosam non expedita perspiciatis iusto.",
+         null,
+         "49.03",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Logged",
+         "Medium",
+         "Nehmat Joshi",
+         "Opera/8.48.(Windows NT 6.0; mt-MT) Presto/2.9.171 Version/12.00",
+         "Segment C",
+         "Medininagar, Gujarat",
+         null,
+         null,
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "76",
+         "142"
+        ],
+        [
+         "39",
+         "2023-03-14 07:27:32",
+         "118.23.216.0",
+         "109.17.204.56",
+         "5032",
+         "1068",
+         "TCP",
+         "308",
+         "Control",
+         "DNS",
+         "Debitis ut occaecati nostrum tempore sunt illum. Repudiandae dolorum reiciendis aperiam. Minima sequi alias doloremque ducimus.\nIllo officia molestiae voluptas. Rem ullam placeat ea.",
+         null,
+         "85.28",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern A",
+         "Blocked",
+         "Medium",
+         "Ojas Ray",
+         "Mozilla/5.0 (iPhone; CPU iPhone OS 4_2_1 like Mac OS X) AppleWebKit/534.2 (KHTML, like Gecko) FxiOS/10.8u7697.0 Mobile/58J944 Safari/534.2",
+         "Segment A",
+         "Ichalkaranji, Sikkim",
+         "153.251.24.124",
+         null,
+         null,
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "118",
+         "109"
+        ],
+        [
+         "40",
+         "2021-02-13 16:45:36",
+         "212.153.110.146",
+         "59.138.247.230",
+         "58316",
+         "54095",
+         "TCP",
+         "545",
+         "Data",
+         "DNS",
+         "Officia odio expedita. Ab facilis recusandae debitis labore eaque non impedit. Maiores eveniet in ipsam ducimus consequuntur cupiditate.",
+         null,
+         "6.44",
+         null,
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "Medium",
+         "Ela Guha",
+         "Mozilla/5.0 (iPod; U; CPU iPhone OS 3_0 like Mac OS X; ber-DZ) AppleWebKit/535.35.7 (KHTML, like Gecko) Version/3.0.5 Mobile/8B117 Safari/6535.35.7",
+         "Segment A",
+         "Bidar, Maharashtra",
+         null,
+         null,
+         "Alert Data",
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Dynamic (49152-65535)",
+         "Dynamic (49152-65535)",
+         "Low (0-25%)",
+         "212",
+         "59"
+        ],
+        [
+         "41",
+         "2021-05-23 16:29:26",
+         "6.64.251.220",
+         "137.104.11.138",
+         "26896",
+         "45972",
+         "UDP",
+         "572",
+         "Control",
+         "DNS",
+         "Voluptatem aliquam sit. Asperiores quae doloremque at sit dolorem. Laboriosam necessitatibus in magni.",
+         null,
+         "22.17",
+         null,
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "Low",
+         "Keya Biswas",
+         "Mozilla/5.0 (compatible; MSIE 6.0; Windows CE; Trident/5.0)",
+         "Segment B",
+         "Dewas, Uttar Pradesh",
+         "172.194.101.64",
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Low (0-25%)",
+         "6",
+         "137"
+        ],
+        [
+         "42",
+         "2022-12-01 08:47:07",
+         "170.112.189.99",
+         "21.28.78.79",
+         "1031",
+         "40417",
+         "TCP",
+         "1474",
+         "Control",
+         "HTTP",
+         "Impedit itaque debitis repellendus. Reprehenderit ad dolore officiis natus placeat. Ducimus nostrum perferendis praesentium.",
+         "IoC Detected",
+         "29.66",
+         null,
+         "DDoS",
+         "Known Pattern B",
+         "Logged",
+         "Low",
+         "Trisha Rajagopal",
+         "Mozilla/5.0 (Linux; Android 4.4) AppleWebKit/534.2 (KHTML, like Gecko) Chrome/16.0.840.0 Safari/534.2",
+         "Segment A",
+         "Adoni, Tripura",
+         null,
+         null,
+         null,
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "170",
+         "21"
+        ],
+        [
+         "43",
+         "2022-07-21 20:33:30",
+         "50.30.140.36",
+         "76.154.100.68",
+         "57124",
+         "48417",
+         "UDP",
+         "1363",
+         "Data",
+         "FTP",
+         "Nulla magnam minima dolor similique tempore assumenda. Voluptatum ad voluptate expedita.",
+         "IoC Detected",
+         "10.75",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern A",
+         "Blocked",
+         "High",
+         "Farhan Mahal",
+         "Mozilla/5.0 (Windows; U; Windows NT 5.0) AppleWebKit/531.24.5 (KHTML, like Gecko) Version/5.0.4 Safari/531.24.5",
+         "Segment A",
+         "Eluru, Uttarakhand",
+         null,
+         "Log Data",
+         null,
+         "Server",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "Low (0-25%)",
+         "50",
+         "76"
+        ],
+        [
+         "44",
+         "2023-06-09 05:57:13",
+         "194.120.14.24",
+         "201.55.168.126",
+         "32979",
+         "3852",
+         "UDP",
+         "443",
+         "Data",
+         "DNS",
+         "Dicta ex ex asperiores consequuntur. Nostrum beatae aspernatur atque assumenda necessitatibus dolore.\nInventore inventore laborum praesentium. Laborum quisquam tenetur minima.",
+         null,
+         "42.14",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Ignored",
+         "High",
+         "Zara Kar",
+         "Mozilla/5.0 (Macintosh; PPC Mac OS X 10_12_5; rv:1.9.3.20) Gecko/2197-07-11 00:07:59 Firefox/14.0",
+         "Segment A",
+         "Parbhani, Rajasthan",
+         "16.90.191.251",
+         "Log Data",
+         "Alert Data",
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "256-512",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "194",
+         "201"
+        ],
+        [
+         "45",
+         "2020-04-10 18:21:31",
+         "69.29.17.248",
+         "85.236.61.31",
+         "59738",
+         "44531",
+         "TCP",
+         "1026",
+         "Data",
+         "FTP",
+         "Esse harum consequuntur numquam sint tempore ducimus. Autem numquam nostrum id mollitia veritatis. Incidunt odit perspiciatis hic placeat aut accusantium. Possimus voluptatibus consectetur quis.",
+         null,
+         "77.73",
+         null,
+         "DDoS",
+         "Known Pattern B",
+         "Logged",
+         "Low",
+         "Samar Loyal",
+         "Opera/9.51.(X11; Linux i686; byn-ER) Presto/2.9.185 Version/12.00",
+         "Segment A",
+         "Panvel, West Bengal",
+         null,
+         null,
+         null,
+         "Firewall",
+         "0",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "Critical (75-100%)",
+         "69",
+         "85"
+        ],
+        [
+         "46",
+         "2023-05-16 13:01:56",
+         "170.211.138.30",
+         "172.97.181.148",
+         "25022",
+         "6593",
+         "TCP",
+         "554",
+         "Control",
+         "DNS",
+         "Voluptate mollitia cupiditate necessitatibus nisi expedita pariatur. Recusandae dolorum eligendi qui placeat placeat molestiae dolorum. Odio sequi aut deleniti eius molestias.",
+         "IoC Detected",
+         "97.82",
+         "Alert Triggered",
+         "Malware",
+         "Known Pattern B",
+         "Blocked",
+         "High",
+         "Aradhya Kamdar",
+         "Mozilla/5.0 (iPod; U; CPU iPhone OS 3_3 like Mac OS X; sk-SK) AppleWebKit/531.45.6 (KHTML, like Gecko) Version/3.0.5 Mobile/8B112 Safari/6531.45.6",
+         "Segment A",
+         "Amravati, Kerala",
+         "95.170.137.42",
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "170",
+         "172"
+        ],
+        [
+         "47",
+         "2020-10-08 04:34:34",
+         "107.247.253.62",
+         "45.35.146.21",
+         "10584",
+         "23178",
+         "ICMP",
+         "649",
+         "Data",
+         "HTTP",
+         "Velit quis ad a aliquam veniam laboriosam dignissimos. Assumenda tenetur maiores animi harum iusto aut. Itaque quidem voluptas quidem.",
+         "IoC Detected",
+         "83.36",
+         "Alert Triggered",
+         "Intrusion",
+         "Known Pattern A",
+         "Blocked",
+         "High",
+         "Hansh Chaudhari",
+         "Mozilla/5.0 (X11; Linux i686; rv:1.9.5.20) Gecko/7083-06-02 15:24:24 Firefox/3.6.9",
+         "Segment C",
+         "Vijayawada, Bihar",
+         "197.238.110.123",
+         null,
+         "Alert Data",
+         "Server",
+         "1",
+         "False",
+         "False",
+         "False",
+         "512-1024",
+         "Registered (1024-49151)",
+         "Registered (1024-49151)",
+         "Critical (75-100%)",
+         "107",
+         "45"
+        ],
+        [
+         "48",
+         "2021-08-20 14:42:57",
+         "118.175.170.127",
+         "43.100.128.47",
+         "23934",
+         "57206",
+         "TCP",
+         "1378",
+         "Control",
+         "DNS",
+         "Pariatur quia dolore praesentium. Ut dolorem nihil nulla.\nAb unde maiores deserunt tempora. Debitis numquam sed nisi. Vitae deleniti cum.",
+         "IoC Detected",
+         "40.71",
+         null,
+         "Malware",
+         "Known Pattern A",
+         "Blocked",
+         "Medium",
+         "Nishith Tandon",
+         "Mozilla/5.0 (compatible; MSIE 5.0; Windows NT 10.0; Trident/4.1)",
+         "Segment C",
+         "Giridih, Chhattisgarh",
+         "20.86.235.147",
+         "Log Data",
+         "Alert Data",
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "1024-2048",
+         "Dynamic (49152-65535)",
+         "Registered (1024-49151)",
+         "Medium (25-50%)",
+         "118",
+         "43"
+        ],
+        [
+         "49",
+         "2021-05-08 04:56:17",
+         "16.65.249.237",
+         "24.79.26.247",
+         "57819",
+         "10509",
+         "ICMP",
+         "107",
+         "Data",
+         "HTTP",
+         "Repellendus tempora rerum sed numquam vel accusantium. Sunt tempore ut ipsa vero sequi perspiciatis.",
+         null,
+         "1.33",
+         "Alert Triggered",
+         "DDoS",
+         "Known Pattern B",
+         "Ignored",
+         "High",
+         "Ishita Tella",
+         "Mozilla/5.0 (compatible; MSIE 7.0; Windows NT 6.2; Trident/5.0)",
+         "Segment A",
+         "Nashik, Assam",
+         "131.208.67.33",
+         "Log Data",
+         null,
+         "Firewall",
+         "1",
+         "False",
+         "False",
+         "False",
+         "64-128",
+         "Registered (1024-49151)",
+         "Dynamic (49152-65535)",
+         "Low (0-25%)",
+         "16",
+         "24"
+        ]
+       ],
+       "shape": {
+        "columns": 35,
+        "rows": 40000
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Timestamp</th>\n",
+       "      <th>Source IP Address</th>\n",
+       "      <th>Destination IP Address</th>\n",
+       "      <th>Source Port</th>\n",
+       "      <th>Destination Port</th>\n",
+       "      <th>Protocol</th>\n",
+       "      <th>Packet Length</th>\n",
+       "      <th>Packet Type</th>\n",
+       "      <th>Traffic Type</th>\n",
+       "      <th>Payload Data</th>\n",
+       "      <th>...</th>\n",
+       "      <th>has_proxy</th>\n",
+       "      <th>is_bidirectional</th>\n",
+       "      <th>src_is_private</th>\n",
+       "      <th>dst_is_private</th>\n",
+       "      <th>packet_length_bin</th>\n",
+       "      <th>dst_port_category</th>\n",
+       "      <th>src_port_category</th>\n",
+       "      <th>anomaly_category</th>\n",
+       "      <th>src_ip_class</th>\n",
+       "      <th>dst_ip_class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-05-30 06:33:58</td>\n",
+       "      <td>103.216.15.12</td>\n",
+       "      <td>84.9.164.252</td>\n",
+       "      <td>31225</td>\n",
+       "      <td>17616</td>\n",
+       "      <td>ICMP</td>\n",
+       "      <td>503</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Qui natus odio asperiores nam. Optio nobis ius...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>256-512</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Medium (25-50%)</td>\n",
+       "      <td>103</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-08-26 07:08:30</td>\n",
+       "      <td>78.199.217.198</td>\n",
+       "      <td>66.191.137.154</td>\n",
+       "      <td>17245</td>\n",
+       "      <td>48166</td>\n",
+       "      <td>ICMP</td>\n",
+       "      <td>1174</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Aperiam quos modi officiis veritatis rem. Omni...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1024-2048</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>High (50-75%)</td>\n",
+       "      <td>78</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-11-13 08:23:25</td>\n",
+       "      <td>63.79.210.48</td>\n",
+       "      <td>198.219.82.17</td>\n",
+       "      <td>16811</td>\n",
+       "      <td>53600</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>306</td>\n",
+       "      <td>Control</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Perferendis sapiente vitae soluta. Hic delectu...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>256-512</td>\n",
+       "      <td>Dynamic (49152-65535)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Critical (75-100%)</td>\n",
+       "      <td>63</td>\n",
+       "      <td>198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-07-02 10:38:46</td>\n",
+       "      <td>163.42.196.10</td>\n",
+       "      <td>101.228.192.255</td>\n",
+       "      <td>20018</td>\n",
+       "      <td>32534</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>385</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Totam maxime beatae expedita explicabo porro l...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>256-512</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Low (0-25%)</td>\n",
+       "      <td>163</td>\n",
+       "      <td>101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-07-16 13:11:07</td>\n",
+       "      <td>71.166.185.76</td>\n",
+       "      <td>189.243.174.238</td>\n",
+       "      <td>6131</td>\n",
+       "      <td>26646</td>\n",
+       "      <td>TCP</td>\n",
+       "      <td>1462</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>DNS</td>\n",
+       "      <td>Odit nesciunt dolorem nisi iste iusto. Animi v...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1024-2048</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Low (0-25%)</td>\n",
+       "      <td>71</td>\n",
+       "      <td>189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39995</th>\n",
+       "      <td>2023-05-26 14:08:42</td>\n",
+       "      <td>26.36.109.26</td>\n",
+       "      <td>121.100.75.240</td>\n",
+       "      <td>31005</td>\n",
+       "      <td>6764</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>1428</td>\n",
+       "      <td>Control</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Quibusdam ullam consequatur consequuntur accus...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1024-2048</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Medium (25-50%)</td>\n",
+       "      <td>26</td>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39996</th>\n",
+       "      <td>2023-03-27 00:38:27</td>\n",
+       "      <td>17.21.163.81</td>\n",
+       "      <td>196.108.134.78</td>\n",
+       "      <td>2553</td>\n",
+       "      <td>28091</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>1184</td>\n",
+       "      <td>Control</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Quaerat neque esse. Animi expedita natus commo...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1024-2048</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Medium (25-50%)</td>\n",
+       "      <td>17</td>\n",
+       "      <td>196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39997</th>\n",
+       "      <td>2022-03-31 01:45:49</td>\n",
+       "      <td>162.35.217.57</td>\n",
+       "      <td>98.107.0.15</td>\n",
+       "      <td>22505</td>\n",
+       "      <td>25152</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>1043</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>DNS</td>\n",
+       "      <td>Enim at aspernatur illum. Saepe numquam eligen...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1024-2048</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Medium (25-50%)</td>\n",
+       "      <td>162</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39998</th>\n",
+       "      <td>2023-09-22 18:32:38</td>\n",
+       "      <td>208.72.233.205</td>\n",
+       "      <td>173.79.112.252</td>\n",
+       "      <td>20013</td>\n",
+       "      <td>2703</td>\n",
+       "      <td>UDP</td>\n",
+       "      <td>483</td>\n",
+       "      <td>Data</td>\n",
+       "      <td>FTP</td>\n",
+       "      <td>Officiis dolorem sed harum provident earum dis...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>256-512</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Registered (1024-49151)</td>\n",
+       "      <td>Critical (75-100%)</td>\n",
+       "      <td>208</td>\n",
+       "      <td>173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39999</th>\n",
+       "      <td>2023-10-10 11:59:52</td>\n",
+       "      <td>14.102.21.108</td>\n",
+       "      <td>109.198.45.7</td>\n",
+       "      <td>50137</td>\n",
+       "      <td>55575</td>\n",
+       "      <td>ICMP</td>\n",
+       "      <td>1175</td>\n",
+       "      <td>Control</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>Eligendi omnis voluptate nihil voluptatibus do...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1024-2048</td>\n",
+       "      <td>Dynamic (49152-65535)</td>\n",
+       "      <td>Dynamic (49152-65535)</td>\n",
+       "      <td>Medium (25-50%)</td>\n",
+       "      <td>14</td>\n",
+       "      <td>109</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>40000 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Timestamp Source IP Address Destination IP Address  \\\n",
+       "0      2023-05-30 06:33:58     103.216.15.12           84.9.164.252   \n",
+       "1      2020-08-26 07:08:30    78.199.217.198         66.191.137.154   \n",
+       "2      2022-11-13 08:23:25      63.79.210.48          198.219.82.17   \n",
+       "3      2023-07-02 10:38:46     163.42.196.10        101.228.192.255   \n",
+       "4      2023-07-16 13:11:07     71.166.185.76        189.243.174.238   \n",
+       "...                    ...               ...                    ...   \n",
+       "39995  2023-05-26 14:08:42      26.36.109.26         121.100.75.240   \n",
+       "39996  2023-03-27 00:38:27      17.21.163.81         196.108.134.78   \n",
+       "39997  2022-03-31 01:45:49     162.35.217.57            98.107.0.15   \n",
+       "39998  2023-09-22 18:32:38    208.72.233.205         173.79.112.252   \n",
+       "39999  2023-10-10 11:59:52     14.102.21.108           109.198.45.7   \n",
+       "\n",
+       "       Source Port  Destination Port Protocol  Packet Length Packet Type  \\\n",
+       "0            31225             17616     ICMP            503        Data   \n",
+       "1            17245             48166     ICMP           1174        Data   \n",
+       "2            16811             53600      UDP            306     Control   \n",
+       "3            20018             32534      UDP            385        Data   \n",
+       "4             6131             26646      TCP           1462        Data   \n",
+       "...            ...               ...      ...            ...         ...   \n",
+       "39995        31005              6764      UDP           1428     Control   \n",
+       "39996         2553             28091      UDP           1184     Control   \n",
+       "39997        22505             25152      UDP           1043        Data   \n",
+       "39998        20013              2703      UDP            483        Data   \n",
+       "39999        50137             55575     ICMP           1175     Control   \n",
+       "\n",
+       "      Traffic Type                                       Payload Data  ...  \\\n",
+       "0             HTTP  Qui natus odio asperiores nam. Optio nobis ius...  ...   \n",
+       "1             HTTP  Aperiam quos modi officiis veritatis rem. Omni...  ...   \n",
+       "2             HTTP  Perferendis sapiente vitae soluta. Hic delectu...  ...   \n",
+       "3             HTTP  Totam maxime beatae expedita explicabo porro l...  ...   \n",
+       "4              DNS  Odit nesciunt dolorem nisi iste iusto. Animi v...  ...   \n",
+       "...            ...                                                ...  ...   \n",
+       "39995         HTTP  Quibusdam ullam consequatur consequuntur accus...  ...   \n",
+       "39996         HTTP  Quaerat neque esse. Animi expedita natus commo...  ...   \n",
+       "39997          DNS  Enim at aspernatur illum. Saepe numquam eligen...  ...   \n",
+       "39998          FTP  Officiis dolorem sed harum provident earum dis...  ...   \n",
+       "39999         HTTP  Eligendi omnis voluptate nihil voluptatibus do...  ...   \n",
+       "\n",
+       "      has_proxy  is_bidirectional src_is_private dst_is_private  \\\n",
+       "0             1             False          False          False   \n",
+       "1             0             False          False          False   \n",
+       "2             1             False          False          False   \n",
+       "3             0             False          False          False   \n",
+       "4             1             False          False          False   \n",
+       "...         ...               ...            ...            ...   \n",
+       "39995         0             False          False          False   \n",
+       "39996         1             False          False          False   \n",
+       "39997         0             False          False          False   \n",
+       "39998         1             False          False          False   \n",
+       "39999         1             False          False          False   \n",
+       "\n",
+       "      packet_length_bin        dst_port_category        src_port_category  \\\n",
+       "0               256-512  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "1             1024-2048  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "2               256-512    Dynamic (49152-65535)  Registered (1024-49151)   \n",
+       "3               256-512  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "4             1024-2048  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "...                 ...                      ...                      ...   \n",
+       "39995         1024-2048  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "39996         1024-2048  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "39997         1024-2048  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "39998           256-512  Registered (1024-49151)  Registered (1024-49151)   \n",
+       "39999         1024-2048    Dynamic (49152-65535)    Dynamic (49152-65535)   \n",
+       "\n",
+       "         anomaly_category src_ip_class dst_ip_class  \n",
+       "0         Medium (25-50%)          103           84  \n",
+       "1           High (50-75%)           78           66  \n",
+       "2      Critical (75-100%)           63          198  \n",
+       "3             Low (0-25%)          163          101  \n",
+       "4             Low (0-25%)           71          189  \n",
+       "...                   ...          ...          ...  \n",
+       "39995     Medium (25-50%)           26          121  \n",
+       "39996     Medium (25-50%)           17          196  \n",
+       "39997     Medium (25-50%)          162           98  \n",
+       "39998  Critical (75-100%)          208          173  \n",
+       "39999     Medium (25-50%)           14          109  \n",
+       "\n",
+       "[40000 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize bidirectional traffic\n",
+    "if 'Attack Type' in df.columns and 'is_bidirectional' in df.columns:\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    bidir_attack[True].sort_values().plot(kind='barh', color='teal', edgecolor='black')\n",
+    "    plt.xlabel('Percentage of Traffic with Bidirectional IPs', fontsize=12)\n",
+    "    plt.ylabel('Attack Type', fontsize=12)\n",
+    "    plt.title('Bidirectional IP Traffic by Attack Type', fontsize=14, fontweight='bold')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "SPOOFING INDICATOR 4: PRIVATE IP ADDRESS DETECTION\n",
+      "================================================================================\n",
+      "\n",
+      "Private IP Statistics:\n",
+      "  - Source IPs from private ranges: 181 (0.45%)\n",
+      "  - Destination IPs from private ranges: 174 (0.43%)\n",
+      "  - Total connections involving private IPs: 355\n"
+     ]
+    }
+   ],
+   "source": [
+    "# SPOOFING DETECTION 4: Private IP Detection\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"SPOOFING INDICATOR 4: PRIVATE IP ADDRESS DETECTION\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "def is_private_ip(ip):\n",
+    "    \"\"\"Check if an IP is in private range (RFC 1918)\"\"\"\n",
+    "    if pd.isna(ip):\n",
+    "        return False\n",
+    "    try:\n",
+    "        parts = str(ip).split('.')\n",
+    "        if len(parts) != 4:\n",
+    "            return False\n",
+    "        first = int(parts[0])\n",
+    "        second = int(parts[1])\n",
+    "        \n",
+    "        # Private IP ranges: 10.x.x.x, 172.16-31.x.x, 192.168.x.x\n",
+    "        if first == 10:\n",
+    "            return True\n",
+    "        if first == 172 and 16 <= second <= 31:\n",
+    "            return True\n",
+    "        if first == 192 and second == 168:\n",
+    "            return True\n",
+    "        return False\n",
+    "    except:\n",
+    "        return False\n",
+    "\n",
+    "df['src_is_private'] = df['Source IP Address'].apply(is_private_ip)\n",
+    "df['dst_is_private'] = df['Destination IP Address'].apply(is_private_ip)\n",
+    "\n",
+    "print(f\"\\nPrivate IP Statistics:\")\n",
+    "print(f\"  - Source IPs from private ranges: {df['src_is_private'].sum():,} ({df['src_is_private'].sum()/len(df)*100:.2f}%)\")\n",
+    "print(f\"  - Destination IPs from private ranges: {df['dst_is_private'].sum():,} ({df['dst_is_private'].sum()/len(df)*100:.2f}%)\")\n",
+    "print(f\"  - Total connections involving private IPs: {(df['src_is_private'] | df['dst_is_private']).sum():,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Private IP Usage by Attack Type:\n",
+      "             Src_Private_Count  Src_Private_Pct  Dst_Private_Count  \\\n",
+      "Attack Type                                                          \n",
+      "DDoS                        56         0.417039                 68   \n",
+      "Intrusion                   58         0.437241                 49   \n",
+      "Malware                     67         0.503494                 57   \n",
+      "\n",
+      "             Dst_Private_Pct  \n",
+      "Attack Type                   \n",
+      "DDoS                0.506405  \n",
+      "Intrusion           0.369393  \n",
+      "Malware             0.428346  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bJZhIhUwGSPKan3zyyX22zbkNEOrz2PrYeX9nKY7oawm6PK88h7SzBGH152QGXep9ZuZHmwSAeZH+dD1QVVdV5Hsrg3BJaCy55JLVjOh6kk76VJmwkH5q/X1Zf5elT5Pb5bs9if/6u7De5y/9hdZJAvl5dve5yoSgzBxO3yb9l7qflL5Bjqfup9ZVHqmkyHdujqWesJD+XY7lpfo8M5P7zoBt+iRZHiYDurnvDLpmUk/6iIlL6irWVhlAzESa3D790joBk+NrXUt/VtIHzyUDyunTJqarZUZ1XzFXXoe8JokfWisVWm2//fY9k91aK5tbJ2IloZTXvr/Ocb2/RfpgmQmeODP78+XcZuLIvO5/loHw+nylH5q+emvFcl/na17lOdQJjZzvxCHpI6YyO+fu2muv7anOyXu2Tkolnkgckfd2naRIxUGdUMz5rhMa+X3aXuLJDPznNeqtP2PlJBbSB8/rnNilnsCWY0jiJf3y+rXPoHhrkmZOJmLl7+r3WqohMjkoMUueQ+Q8tiYi8v5IHNz6nOuYuLXiIp83+TkxdCTRmvtJPJdxjpz3OuGQz706Vsz/E+envWdM4Mgjj6yeT2LfrIiR1zHv27584xvfqPr8kTGPnO/6ecyLLHVdn8e8f1qTD/W5zrJdvSdi1a9N3v9pq/ncyPsxCbc8r/w7P9okzE8qNaADZAZwkhv5ws5sgswkqqXDlcAkHYYMjuaLue7EpsN70kkn9cysTiCTUth8yWVGQjo28ypLYr361a+uZo33HkzMoH49OJ4v2nQ6UjaZcu10MPJlHPnCTglsPeCdGddnnHFG9VzToU4QNivpALTObOg9+7wOmFql9H52OsI5ryn7TYCYjmd+TuBSP9csPZUOfz1omt+nw/Ha1762ui63TQCQBEIdZOb1aq2GaN3jIs+jriLIpnz1DJx0lNORz8B8XuO+ZvTMrswcyqW3vFfyurUue9bbnHTmWm+bBEUdFCcoTMl+3tcJEPK71tums5ZKmpyvrDWc85H3fc5TArv6PCY5UAfYeR/lbyJlzrm/OlnROjOvXlYowWH9GmU2Vl7PBJd5jWd3j4q0zbSpSMcyzyvHk4GBPM+87/KeyPs5s/mTcEsbSMe7Xq6staKgL5lxmdc90hbqQfcECEkKpS3VG4bn9avXHq7bfe+1iJdYYonp/l//Lp3nHHvOQZJw6QgnAMraxXXCqz7vs9vOe0ulQ530y4BCfe76Q55LXrecq8zQymytvK/qz4V0+uukS6u8TxJA1e0siZYE9wl+EsTmNZ4fbRIA+ks9uJV+Rz3AmP5IEvORAbXLLrusGnBNvyeDiq17yqXyMd9p6aNlQDBLRrX+Pn2H1r7inKwVn+/dfNfW/eBU5tZVDvXgXvp7mSCRxEIeN9/x+V5PX6WWn2fV5+lLJpnUk14yY/yDH/xgT6VLBj3zXDOxJpPGeldhJraqB05z+0z+SLyS85377Cve6Es9o7q39J1alxGrZcZ7+pn5N1WnM5OERvqHiRkzYz39uMy+rydiJQmTOKu/znH6hRngryuJN9544+r/mSGfPl7OTybi5ZzN7WBwkhkzO195zAw295d68lQds0TOY85N4owMcuc2eR1qWdIt/edMkklckeeZc5q+YpKE6acndslr0zo5LoPMdfyc/+e5tA5u93esnNekrr7Icea1zRhC5H7WWGON6n7rCT157LwfM7ZQxwE5hllNxGqdaFVPwEqcnMfPhK5UDOQ9U7+m+Rxo3fcmP7cm0Ga2h0zi2MR0dZydNps2WatjliSc6klZibmyXFOkHSUpkkvvJb8ibaNuNznneY3mdZWLVjnXiafy+Ikz0u4zoaquwpjZXqBJOGeFiMjzzfNLLJKJnYk/E7cNdJuE+UlSAzpEgpBc0sFJeXYG7TILpA5UstRLBtPToaqX7MkSVa1fvlmLP52iDKimk5uOwEstMzSrTbtyf0lotMqAaOvSTbV05FqX3sljpzMV6Qj21VnNl3xmjtRfxrMjX84JSNL5TIct63LmC70v9fqes5LOWzoW6TxlUDwdzgxe18ffeyPG/D6dk3Tc0tFoLRefnT0b6s3UIoFVX/prVnjOVzqJ6bzn9cqA7ayWAmpddqCv90gdSEfr8kMJxpJIe/TRR3tmxGTGTx4vFUW51MFEpAOazl5mNOU937oUVX0eW5f56j2bpa/n0XpuM/O+r6XA5uTc9h64zzHUSZZ0LhPcptOaIKgOTtJxbp2FUy+5NTO5bT1bLH+b4KqWgCmBU9pzytBnNXtqVjKQn/vPJTPGeq97WydiZred91YnNCIDKbOzLu+cSCc/HfskIDJYk7ZZn+sEnX0NdCTYby3dT1tIkJkBnXqW5vxskwAwt+rZ4/WgbOs+Famw7L3UUKSPlYHBTCJJsj4zwTOgl0tkskKqNjMBJAOf8yqxR2v80fr/1j5kvsszizkDdJn93btyYk73Qetr6aJMsGlVTxyJ1iVeaq2Dpr2PvXVZ09mVPnVmT2fQN3249MP7mliU16Z1IP2lpMI8SY302X72s59Vg6X1ZJRM5sos//46x+kP1rdJH7cehG+VweX01/M+mleJK+r4MwPmeW6zGpjN3+R5ziyubX3P1TFOqn9zzjKYn+RWLomp8/7POcxyQa3LFuc5poogybn0H3vvy1b3n+u4JUmkOqFRx5qJ81sr0ZOs6M9YuU5o9l42urV/nrgk8VOWYE3yL5PFckx1FdKsJmIljqjfa/W5rPdvSRKhrlLP+ZxZompOZBA//f2cn3zWtbbB+n3Z2odvjVlmtZRW4rjWmDST6+qVF/pDXrPEH0ny5JzlvTyriViRCpZWiSPryW91+52fbRIGmqQGNFi+PDMTOwOWmSGQwbR03DJLKJd0WlNtkC/DejO3Wa1z29qh66sTmE5XnQjpvXlgb62zKvq6/9ZOYm+zO9Ohr816+5Kkw8xKR2dmTtaSTCCXpEbOTzoHdaCXQdLWTlmSTVkurJ4tkr9LpzGzuOv1OGdlds7N7J6XmcnAc2b2z43Wmf59bQjdel3rfi9JAOQcpMOWDm4SQ3mPZzA+l/yuLofN7KOU9dfJuQTZOY/pXLcOtLcmTWaVhIvZWQd6Ts5t7/d4a1Km/n86jPXspLx3MiOx7tTPKjjovfRU6yBDbwkQ5iWpkWRckgAJbDLjKzPf0nFOB7l1U745aed9SWImAwUJ/jIzLKXp/SWJiwS6Oeac6wRwdQJmZjOe+louon5+9Wfk/GiTADAvstRi3Qerkw+z0+9J3zaDXBnszp5jGdDNd2gmOCRhn/vNEiwZKM/ynvO63E/vwfnWvlPr4GgqKDJomb5kZhinf53BulSFzouX+k6fVZw0O8c+O/pKmMxpzDUzee3Tf8tAb5Ia6YfWg+OJS/rzHM/u/irpI83tAGoGfntvfD+ncUsmVPW1z97M4pYklrJEbfrciU8yEScV12kPuWTpo1RJ59iSFMx5TBvKoHeSLZnklL5+9ltpVcctsxOz9Hes3Lu/3nr/rf/PZKwkNVIhk1itdSLWrJYnTiKkXmorjzezvTPS/6+riOZWlo3LcsVpg4nF08+vN86emTlNPCaWT1IkVfMZZ8i4TH/JceeYs5JG9nRMEqV1Ila9CsSs4pbe76X50SZhfpLUgAZLJyzlgelspWOaDH5reWm+DOsvsrokPAN66YjlCy8dsHRM6w53OnPZ1LYejK/vq3VGUGZ41ffVOkOhL31tApwOT13ymJkq9QyoHE+W+8kXdJZ2yWBuZpGlk51ZUa0zx9J5SOcqHcW5DRZmx5xsdJjZNBlcr8v06yRS1tRv7Qgm2EtCI8FHEhn1uW0txe0rWGrtkKRstJbOcD2TP53DdKTTwZqT4Ka/ZbZYLTOY6rWW+5qxXs+ATycywUBm0aQDVXcKM0skyxflftKRS8Ii77+cx8jMwHr/lzz/1k0uex9LOoSt8lr11tp5SxlxXRYdmaGW37eWnM9KZre1zvqp18PNa9t6bAkQEtxkYCBLHeW55DYzm4XT2hbq9aVnJe0ubbZ3J7h3Z3dm77u81+qZWpmlWc8EShXYvLTzVlneIsm0JEyS0Mqybvnbl1rubE7kuSVASBIm5ztBZx2kzmxJscyES0BaV27lM7Ce1VW/XwZ7mwSALCtSq5d3bO33HHTQQWXvvffu+TmD2Ulk1DP36+/DfAdmkDbf1+m/pWI2lbYZ2Mv3al9JjfQn+nMpkwyO5vjqpSvrDa9b9yfry+wMFrdWW2QyUuvSka3VsP1RldJf+oq5XkoqCRI7pmL8zDPPrK5Lv7S1EqU/znHr+ysTY7KWfy3vpfSNZjY4O7/keSepkdUN6qVhWxN6deVO+v91UqOeMJjb1xuC13uQZInctJUkjNKe0k+ulzrKIHs9UN9X/z3HkoRWqvhz//V7MbF+7z01Eg/1Z6ycREWr1j0zWl/HTJjM3hGJuRK/1ktPzSpm6T0R66Wk/5ykUDZIf6nYpFXr9Ykfc67rWDzv35kleFr78IlZWmUCXa5LFUnrvjZ1rJPPu8QUSepmX4rElhkP6C+pFEnMm+eW2Kt+L77UpLfEN60TwlrfN3muTWiTMCdsFA4NloG+enZxOjQpSc7s5sw8TscklRv1IGS9nmi+qNIZiXTe8gWdDmsGfBOc1Gt1tg5Et86SOOecc6pESDrB9Zqac6Je7zPSwUhwkE7b0UcfXSVUMnBdzzCoB5Rzm8zMT2lnOoCZyZ4Z5/nCTgA1WNQbhufc5DmkM1mvaVmrB93zb0pJ682G6w5h60yZ1jVJM+CfjkY6YwlE69/lHGaWUF7zDMKnc5JZMnmd2iUJnnqDwlRYZE3OdIxzjHl9WzddrysHcr6yFFgC6swCzKyndEgTLNTrGqdDm3OaALp+j6Rzl/dE/s2GhPWyCvU5TLVN9jSJvL+y6VvOZTqedQe3d6lvXS6ejmyOP4mGBArpFKejOicVP7mPdMrzmKkyqSswUpnRmoBM+6wHvetOeB5rVmXMrcFBNghMINT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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Analyze private IP usage by attack type\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    print(\"\\nPrivate IP Usage by Attack Type:\")\n",
+    "    attack_private = df.groupby('Attack Type').agg({\n",
+    "        'src_is_private': ['sum', 'mean'],\n",
+    "        'dst_is_private': ['sum', 'mean']\n",
+    "    })\n",
+    "    attack_private.columns = ['Src_Private_Count', 'Src_Private_Pct', 'Dst_Private_Count', 'Dst_Private_Pct']\n",
+    "    attack_private['Src_Private_Pct'] = attack_private['Src_Private_Pct'] * 100\n",
+    "    attack_private['Dst_Private_Pct'] = attack_private['Dst_Private_Pct'] * 100\n",
+    "    print(attack_private)\n",
+    "    \n",
+    "    # Visualize\n",
+    "    fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "    \n",
+    "    attack_private['Src_Private_Pct'].plot(kind='bar', ax=axes[0], color='orange', edgecolor='black')\n",
+    "    axes[0].set_title('Source Private IP Usage by Attack Type', fontsize=14, fontweight='bold')\n",
+    "    axes[0].set_xlabel('Attack Type')\n",
+    "    axes[0].set_ylabel('Percentage')\n",
+    "    axes[0].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    attack_private['Dst_Private_Pct'].plot(kind='bar', ax=axes[1], color='red', edgecolor='black')\n",
+    "    axes[1].set_title('Destination Private IP Usage by Attack Type', fontsize=14, fontweight='bold')\n",
+    "    axes[1].set_xlabel('Attack Type')\n",
+    "    axes[1].set_ylabel('Percentage')\n",
+    "    axes[1].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IPs appearing at multiple timestamps: 0\n",
+      "IPs in both source and dest: 1\n",
+      "src_ip_class\n",
+      "46     222\n",
+      "146    212\n",
+      "166    211\n",
+      "118    209\n",
+      "63     208\n",
+      "      ... \n",
+      "29     151\n",
+      "95     151\n",
+      "149    147\n",
+      "223    145\n",
+      "100    140\n",
+      "Name: count, Length: 222, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Check if this is time-series data (same IPs at different times)\n",
+    "temporal_reuse = df.groupby('Source IP Address')['Timestamp'].nunique()\n",
+    "print(\"IPs appearing at multiple timestamps:\", (temporal_reuse > 1).sum())\n",
+    "\n",
+    "# 2. Analyze if Source and Destination IPs ever overlap\n",
+    "src_set = set(df['Source IP Address'])\n",
+    "dst_set = set(df['Destination IP Address'])\n",
+    "overlap = src_set.intersection(dst_set)\n",
+    "print(f\"IPs in both source and dest: {len(overlap)}\")  # You found only 1!\n",
+    "\n",
+    "# 3. Check distribution of IP ranges\n",
+    "df['src_ip_class'] = df['Source IP Address'].str.split('.').str[0].astype(int)\n",
+    "df['dst_ip_class'] = df['Destination IP Address'].str.split('.').str[0].astype(int)\n",
+    "print(df['src_ip_class'].value_counts())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Packet Length  Anomaly Scores  Severity Level\n",
+      "has_proxy                                               \n",
+      "0             782.875926       50.086917             0.0\n",
+      "1             780.050573       50.139637             0.0\n",
+      "Chi-square p-value: 0.17083792384904176\n",
+      "Locations with highest proxy usage:\n",
+      "Geo-location Data\n",
+      "Yamunanagar, Andhra Pradesh     1.0\n",
+      "Agartala, Tamil Nadu            1.0\n",
+      "Adoni, Manipur                  1.0\n",
+      "Adoni, Andhra Pradesh           1.0\n",
+      "Yamunanagar, Tamil Nadu         1.0\n",
+      "Yamunanagar, Rajasthan          1.0\n",
+      "Adoni, Meghalaya                1.0\n",
+      "Adoni, Nagaland                 1.0\n",
+      "Buxar, Odisha                   1.0\n",
+      "Siwan, Karnataka                1.0\n",
+      "Sirsa, Tamil Nadu               1.0\n",
+      "Sirsa, Sikkim                   1.0\n",
+      "Sirsa, Meghalaya                1.0\n",
+      "Bulandshahr, Punjab             1.0\n",
+      "Sirsa, Karnataka                1.0\n",
+      "Chandigarh, Maharashtra         1.0\n",
+      "Chandigarh, Jharkhand           1.0\n",
+      "Chandigarh, Himachal Pradesh    1.0\n",
+      "Medininagar, Odisha             1.0\n",
+      "Agra, Haryana                   1.0\n",
+      "Name: has_proxy, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Check if proxy usage correlates with OTHER features instead\n",
+    "correlation_analysis = df.groupby('has_proxy').agg({\n",
+    "    'Packet Length': 'mean',\n",
+    "    'Anomaly Scores': 'mean',\n",
+    "    'Severity Level': lambda x: (x == 'Critical').mean()\n",
+    "})\n",
+    "print(correlation_analysis)\n",
+    "\n",
+    "# 2. Test independence with chi-square\n",
+    "from scipy.stats import chi2_contingency\n",
+    "contingency = pd.crosstab(df['Attack Type'], df['has_proxy'])\n",
+    "chi2, p_value, dof, expected = chi2_contingency(contingency)\n",
+    "print(f\"Chi-square p-value: {p_value}\")\n",
+    "# If p > 0.05, proxy and attack type are independent\n",
+    "\n",
+    "# 3. Analyze proxy usage with geo-location\n",
+    "proxy_geo = df.groupby('Geo-location Data')['has_proxy'].mean().sort_values(ascending=False).head(20)\n",
+    "print(\"Locations with highest proxy usage:\")\n",
+    "print(proxy_geo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ANALYSIS 1: IP CLASS DISTRIBUTION AND ATTACK PATTERNS\n",
+    "# ============================================================================\n",
+    "\n",
+    "def analyze_ip_class_patterns(df):\n",
+    "    \"\"\"\n",
+    "    Analyze if certain IP ranges are associated with specific attack types\n",
+    "    \"\"\"\n",
+    "    print(\"=\"*80)\n",
+    "    print(\"IP CLASS DISTRIBUTION ANALYSIS\")\n",
+    "    print(\"=\"*80)\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "1. Source IP Class Distribution:\n",
+      "   - Total unique first octets: 222\n",
+      "   - Min IPs per class: 140\n",
+      "   - Max IPs per class: 222\n",
+      "   - Mean IPs per class: 180.18\n",
+      "   - Std dev: 14.36\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract IP class (first octet)\n",
+    "df['src_ip_class'] = df['Source IP Address'].str.split('.').str[0].astype(int)\n",
+    "df['dst_ip_class'] = df['Destination IP Address'].str.split('.').str[0].astype(int)\n",
+    "\n",
+    "    \n",
+    "# Source IP class distribution\n",
+    "print(\"\\n1. Source IP Class Distribution:\")\n",
+    "src_class_dist = df['src_ip_class'].value_counts().sort_index()\n",
+    "print(f\"   - Total unique first octets: {len(src_class_dist)}\")\n",
+    "print(f\"   - Min IPs per class: {src_class_dist.min()}\")\n",
+    "print(f\"   - Max IPs per class: {src_class_dist.max()}\")\n",
+    "print(f\"   - Mean IPs per class: {src_class_dist.mean():.2f}\")\n",
+    "print(f\"   - Std dev: {src_class_dist.std():.2f}\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "2. IP Class vs Attack Type Association:\n",
+      "\n",
+      "   IP Class 46 (n=222):\n",
+      "     - Dominant attack: Intrusion (37.4%)\n",
+      "     - Distribution: {'Intrusion': 83, 'Malware': 71, 'DDoS': 68}\n",
+      "\n",
+      "   IP Class 146 (n=212):\n",
+      "     - Dominant attack: DDoS (36.8%)\n",
+      "     - Distribution: {'DDoS': 78, 'Malware': 68, 'Intrusion': 66}\n",
+      "\n",
+      "   IP Class 166 (n=211):\n",
+      "     - Dominant attack: Malware (34.1%)\n",
+      "     - Distribution: {'Malware': 72, 'DDoS': 72, 'Intrusion': 67}\n",
+      "\n",
+      "   IP Class 118 (n=209):\n",
+      "     - Dominant attack: Malware (36.4%)\n",
+      "     - Distribution: {'Malware': 76, 'Intrusion': 73, 'DDoS': 60}\n",
+      "\n",
+      "   IP Class 63 (n=208):\n",
+      "     - Dominant attack: Malware (37.0%)\n",
+      "     - Distribution: {'Malware': 77, 'DDoS': 67, 'Intrusion': 64}\n",
+      "\n",
+      "   IP Class 176 (n=208):\n",
+      "     - Dominant attack: DDoS (37.0%)\n",
+      "     - Distribution: {'DDoS': 77, 'Intrusion': 68, 'Malware': 63}\n",
+      "\n",
+      "   IP Class 67 (n=207):\n",
+      "     - Dominant attack: Intrusion (37.7%)\n",
+      "     - Distribution: {'Intrusion': 78, 'Malware': 65, 'DDoS': 64}\n",
+      "\n",
+      "   IP Class 68 (n=206):\n",
+      "     - Dominant attack: DDoS (37.4%)\n",
+      "     - Distribution: {'DDoS': 77, 'Intrusion': 68, 'Malware': 61}\n",
+      "\n",
+      "   IP Class 213 (n=206):\n",
+      "     - Dominant attack: Intrusion (38.3%)\n",
+      "     - Distribution: {'Intrusion': 79, 'Malware': 64, 'DDoS': 63}\n",
+      "\n",
+      "   IP Class 74 (n=205):\n",
+      "     - Dominant attack: DDoS (35.1%)\n",
+      "     - Distribution: {'DDoS': 72, 'Intrusion': 69, 'Malware': 64}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "✓ Visualization saved as 'ip_class_analysis.png'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check if certain IP classes are associated with specific attacks\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    print(\"\\n2. IP Class vs Attack Type Association:\")\n",
+    "        \n",
+    "# Get top 10 most common source IP classes\n",
+    "top_10_classes = df['src_ip_class'].value_counts().head(10).index\n",
+    "        \n",
+    "for ip_class in top_10_classes:\n",
+    "    subset = df[df['src_ip_class'] == ip_class]\n",
+    "    attack_dist = subset['Attack Type'].value_counts()\n",
+    "    dominant_attack = attack_dist.index[0]\n",
+    "    dominant_pct = (attack_dist.values[0] / len(subset)) * 100\n",
+    "            \n",
+    "    print(f\"\\n   IP Class {ip_class} (n={len(subset)}):\")\n",
+    "    print(f\"     - Dominant attack: {dominant_attack} ({dominant_pct:.1f}%)\")\n",
+    "    print(f\"     - Distribution: {attack_dist.to_dict()}\")\n",
+    "            \n",
+    "# Check if this is significantly different from overall distribution\n",
+    "if dominant_pct > 40:  # If >40% is one type\n",
+    "    print(f\"     ⚠️  PATTERN DETECTED: {ip_class}.x.x.x specializes in {dominant_attack}\")\n",
+    "    \n",
+    "# Visualize IP class distribution by attack type\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "    \n",
+    "# Source IP class distribution\n",
+    "top_20_src_classes = df['src_ip_class'].value_counts().head(20)\n",
+    "axes[0].bar(range(len(top_20_src_classes)), top_20_src_classes.values, \n",
+    "                color='steelblue', edgecolor='black')\n",
+    "axes[0].set_xticks(range(len(top_20_src_classes)))\n",
+    "axes[0].set_xticklabels(top_20_src_classes.index, rotation=45)\n",
+    "axes[0].set_xlabel('IP Class (First Octet)', fontsize=12)\n",
+    "axes[0].set_ylabel('Count', fontsize=12)\n",
+    "axes[0].set_title('Top 20 Source IP Classes', fontsize=14, fontweight='bold')\n",
+    "axes[0].grid(axis='y', alpha=0.3)\n",
+    "    \n",
+    "# IP class vs attack type heatmap\n",
+    "if 'Attack Type' in df.columns:\n",
+    "        ip_attack_matrix = pd.crosstab(\n",
+    "            df['src_ip_class'], \n",
+    "            df['Attack Type'], \n",
+    "            normalize='index'\n",
+    "        ) * 100\n",
+    "        \n",
+    "# Get top 15 IP classes for readability\n",
+    "top_15_classes = df['src_ip_class'].value_counts().head(15).index\n",
+    "ip_attack_subset = ip_attack_matrix.loc[top_15_classes]\n",
+    "        \n",
+    "sns.heatmap(ip_attack_subset, annot=True, fmt='.1f', cmap='YlOrRd', \n",
+    "    ax=axes[1], cbar_kws={'label': 'Percentage'})\n",
+    "axes[1].set_xlabel('Attack Type', fontsize=12)\n",
+    "axes[1].set_ylabel('IP Class (First Octet)', fontsize=12)\n",
+    "axes[1].set_title('Attack Type Distribution by IP Class (%)', \n",
+    "                         fontsize=14, fontweight='bold')\n",
+    "    \n",
+    "plt.tight_layout()\n",
+    "plt.savefig('ip_class_analysis.png', dpi=300, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "print(\"\\n✓ Visualization saved as 'ip_class_analysis.png'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Severity Level\n",
+      "Medium    13435\n",
+      "High      13382\n",
+      "Low       13183\n",
+      "Name: count, dtype: int64\n",
+      "['Low' 'Medium' 'High']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the actual values in Severity Level\n",
+    "print(df['Severity Level'].value_counts())\n",
+    "print(df['Severity Level'].unique())\n",
+    "\n",
+    "# It might be categorical ('Low', 'Medium', 'High') not numeric\n",
+    "# Or it might all be zeros/NaN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Protocol Usage by Attack Type (%):\n",
+      "Protocol          ICMP        TCP        UDP\n",
+      "Attack Type                                 \n",
+      "DDoS         33.571641  33.050343  33.378016\n",
+      "Intrusion    33.622314  33.147380  33.230305\n",
+      "Malware      33.523709  33.343353  33.132938\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "protocol_attack = pd.crosstab(df['Attack Type'], df['Protocol'], normalize='index') * 100\n",
+    "print(\"\\nProtocol Usage by Attack Type (%):\")\n",
+    "print(protocol_attack)\n",
+    "\n",
+    "# Visualize\n",
+    "protocol_attack.plot(kind='bar', stacked=True, figsize=(10, 6))\n",
+    "plt.title('Protocol Distribution by Attack Type')\n",
+    "plt.ylabel('Percentage')\n",
+    "plt.legend(title='Protocol')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Packet Length ANOVA: F=1.05, p=3.5168e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Statistical test\n",
+    "from scipy.stats import f_oneway\n",
+    "\n",
+    "ddos_packets = df[df['Attack Type'] == 'DDoS']['Packet Length']\n",
+    "malware_packets = df[df['Attack Type'] == 'Malware']['Packet Length']\n",
+    "intrusion_packets = df[df['Attack Type'] == 'Intrusion']['Packet Length']\n",
+    "\n",
+    "f_stat, p_value = f_oneway(ddos_packets, malware_packets, intrusion_packets)\n",
+    "print(f\"\\nPacket Length ANOVA: F={f_stat:.2f}, p={p_value:.4e}\")\n",
+    "\n",
+    "if p_value < 0.001:\n",
+    "    print(\"✓ Packet Length is HIGHLY discriminative!\")\n",
+    "    \n",
+    "# Show distributions\n",
+    "df.boxplot(column='Packet Length', by='Attack Type', figsize=(10, 6))\n",
+    "plt.title('Packet Length by Attack Type')\n",
+    "plt.suptitle('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Top 10 Destination Ports by Attack Type:\n",
+      "\n",
+      "Malware:\n",
+      "  Port 17890: 4 times (0.0%)\n",
+      "  Port 15559: 4 times (0.0%)\n",
+      "  Port 41043: 4 times (0.0%)\n",
+      "  Port 17896: 4 times (0.0%)\n",
+      "  Port 50417: 4 times (0.0%)\n",
+      "  Port 8226: 4 times (0.0%)\n",
+      "  Port 7550: 3 times (0.0%)\n",
+      "  Port 14719: 3 times (0.0%)\n",
+      "  Port 24994: 3 times (0.0%)\n",
+      "  Port 62717: 3 times (0.0%)\n",
+      "\n",
+      "DDoS:\n",
+      "  Port 25319: 4 times (0.0%)\n",
+      "  Port 45110: 3 times (0.0%)\n",
+      "  Port 37758: 3 times (0.0%)\n",
+      "  Port 9565: 3 times (0.0%)\n",
+      "  Port 38472: 3 times (0.0%)\n",
+      "  Port 46157: 3 times (0.0%)\n",
+      "  Port 56552: 3 times (0.0%)\n",
+      "  Port 41588: 3 times (0.0%)\n",
+      "  Port 23924: 3 times (0.0%)\n",
+      "  Port 4523: 3 times (0.0%)\n",
+      "\n",
+      "Intrusion:\n",
+      "  Port 33573: 4 times (0.0%)\n",
+      "  Port 22269: 4 times (0.0%)\n",
+      "  Port 14648: 4 times (0.0%)\n",
+      "  Port 18787: 4 times (0.0%)\n",
+      "  Port 5048: 4 times (0.0%)\n",
+      "  Port 12194: 3 times (0.0%)\n",
+      "  Port 33177: 3 times (0.0%)\n",
+      "  Port 54203: 3 times (0.0%)\n",
+      "  Port 19110: 3 times (0.0%)\n",
+      "  Port 51094: 3 times (0.0%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nTop 10 Destination Ports by Attack Type:\")\n",
+    "\n",
+    "for attack in df['Attack Type'].unique():\n",
+    "    print(f\"\\n{attack}:\")\n",
+    "    top_ports = df[df['Attack Type'] == attack]['Destination Port'].value_counts().head(10)\n",
+    "    for port, count in top_ports.items():\n",
+    "        pct = (count / len(df[df['Attack Type'] == attack])) * 100\n",
+    "        print(f\"  Port {port}: {count} times ({pct:.1f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Severity Distribution by Attack Type (%):\n",
+      "Severity Level       High        Low     Medium\n",
+      "Attack Type                                    \n",
+      "DDoS            33.683348  33.139708  33.176944\n",
+      "Intrusion       33.373539  32.973992  33.652469\n",
+      "Malware         33.305779  32.757195  33.937026\n",
+      "\n",
+      "Chi-square: 1.80, p-value: 7.7301e-01\n",
+      "✗ Severity is INDEPENDENT of attack type\n"
+     ]
+    }
+   ],
+   "source": [
+    "severity_attack = pd.crosstab(df['Attack Type'], df['Severity Level'], normalize='index') * 100\n",
+    "print(\"\\nSeverity Distribution by Attack Type (%):\")\n",
+    "print(severity_attack)\n",
+    "\n",
+    "# Chi-square test\n",
+    "from scipy.stats import chi2_contingency\n",
+    "contingency = pd.crosstab(df['Attack Type'], df['Severity Level'])\n",
+    "chi2, p_value, dof, expected = chi2_contingency(contingency)\n",
+    "print(f\"\\nChi-square: {chi2:.2f}, p-value: {p_value:.4e}\")\n",
+    "\n",
+    "if p_value < 0.05:\n",
+    "    print(\"✓ Severity is DEPENDENT on attack type\")\n",
+    "else:\n",
+    "    print(\"✗ Severity is INDEPENDENT of attack type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Anomaly Scores ANOVA: F=0.27, p=7.6600e-01\n",
+      "\n",
+      "Mean Anomaly Scores:\n",
+      "Attack Type\n",
+      "DDoS         50.235063\n",
+      "Intrusion    49.978029\n",
+      "Malware      50.125794\n",
+      "Name: Anomaly Scores, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from scipy.stats import f_oneway\n",
+    "\n",
+    "ddos_anomaly = df[df['Attack Type'] == 'DDoS']['Anomaly Scores']\n",
+    "malware_anomaly = df[df['Attack Type'] == 'Malware']['Anomaly Scores']\n",
+    "intrusion_anomaly = df[df['Attack Type'] == 'Intrusion']['Anomaly Scores']\n",
+    "\n",
+    "f_stat, p_value = f_oneway(ddos_anomaly, malware_anomaly, intrusion_anomaly)\n",
+    "print(f\"Anomaly Scores ANOVA: F={f_stat:.2f}, p={p_value:.4e}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\nMean Anomaly Scores:\")\n",
+    "print(df.groupby('Attack Type')['Anomaly Scores'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "GEO-LOCATION ANALYSIS\n",
+      "================================================================================\n",
+      "\n",
+      "Geo-location Statistics:\n",
+      "  - Unique locations: 8,723\n",
+      "  - Missing values: 0\n",
+      "\n",
+      "Top 15 Geo-locations:\n",
+      "Geo-location Data\n",
+      "Ghaziabad, Meghalaya                       16\n",
+      "Kalyan-Dombivli, Jharkhand                 15\n",
+      "Ghaziabad, Uttarakhand                     14\n",
+      "Ghaziabad, Tripura                         14\n",
+      "Motihari, Odisha                           13\n",
+      "Srikakulam, Uttarakhand                    13\n",
+      "Yamunanagar, Arunachal Pradesh             13\n",
+      "Kottayam, Nagaland                         13\n",
+      "Ghaziabad, Jharkhand                       13\n",
+      "Aurangabad, Nagaland                       13\n",
+      "Amroha, Sikkim                             13\n",
+      "Raurkela Industrial Township, Jharkhand    12\n",
+      "Patna, Karnataka                           12\n",
+      "Ghaziabad, Nagaland                        12\n",
+      "Bally, Maharashtra                         12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Top Geo-location by Attack Type:\n",
+      "  Malware: Aligarh, Chhattisgarh (9 occurrences)\n",
+      "  DDoS: Raichur, Sikkim (7 occurrences)\n",
+      "  Intrusion: Ghaziabad, Jharkhand (10 occurrences)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Geo-location analysis (if available)\n",
+    "if 'Geo-location Data' in df.columns:\n",
+    "    print(\"\\n\" + \"=\"*80)\n",
+    "    print(\"GEO-LOCATION ANALYSIS\")\n",
+    "    print(\"=\"*80)\n",
+    "    \n",
+    "    print(f\"\\nGeo-location Statistics:\")\n",
+    "    print(f\"  - Unique locations: {df['Geo-location Data'].nunique():,}\")\n",
+    "    print(f\"  - Missing values: {df['Geo-location Data'].isna().sum():,}\")\n",
+    "    \n",
+    "    # Top locations\n",
+    "    print(f\"\\nTop 15 Geo-locations:\")\n",
+    "    top_locations = df['Geo-location Data'].value_counts().head(15)\n",
+    "    print(top_locations)\n",
+    "    \n",
+    "    # Visualize\n",
+    "    plt.figure(figsize=(14, 8))\n",
+    "    top_locations.plot(kind='barh', color='skyblue', edgecolor='black')\n",
+    "    plt.xlabel('Frequency', fontsize=12)\n",
+    "    plt.ylabel('Geo-location', fontsize=12)\n",
+    "    plt.title('Top 15 Geo-locations in Attack Traffic', fontsize=14, fontweight='bold')\n",
+    "    plt.gca().invert_yaxis()\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # Geo-location by attack type\n",
+    "    if 'Attack Type' in df.columns:\n",
+    "        print(f\"\\nTop Geo-location by Attack Type:\")\n",
+    "        for attack in df['Attack Type'].unique():\n",
+    "            top_loc = df[df['Attack Type'] == attack]['Geo-location Data'].value_counts().head(1)\n",
+    "            if len(top_loc) > 0:\n",
+    "                print(f\"  {attack}: {top_loc.index[0]} ({top_loc.values[0]} occurrences)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 4. PART 3: Data Bin Trends Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================================\n",
+      "DATA BIN TRENDS ANALYSIS\n",
+      "================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"=\"*80)\n",
+    "print(\"DATA BIN TRENDS ANALYSIS\")\n",
+    "print(\"=\"*80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "PACKET LENGTH DISTRIBUTION\n",
+      "================================================================================\n",
+      "\n",
+      "Packet Length Statistics:\n",
+      "  - Mean: 781.45 bytes\n",
+      "  - Median: 782.00 bytes\n",
+      "  - Std Dev: 416.04 bytes\n",
+      "  - Min: 64.00 bytes\n",
+      "  - Max: 1500.00 bytes\n",
+      "  - 25th percentile: 420.00 bytes\n",
+      "  - 75th percentile: 1143.00 bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. PACKET LENGTH ANALYSIS\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"PACKET LENGTH DISTRIBUTION\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "if 'Packet Length' in df.columns:\n",
+    "    packet_lengths = df['Packet Length'].dropna()\n",
+    "    \n",
+    "    print(f\"\\nPacket Length Statistics:\")\n",
+    "    print(f\"  - Mean: {packet_lengths.mean():.2f} bytes\")\n",
+    "    print(f\"  - Median: {packet_lengths.median():.2f} bytes\")\n",
+    "    print(f\"  - Std Dev: {packet_lengths.std():.2f} bytes\")\n",
+    "    print(f\"  - Min: {packet_lengths.min():.2f} bytes\")\n",
+    "    print(f\"  - Max: {packet_lengths.max():.2f} bytes\")\n",
+    "    print(f\"  - 25th percentile: {packet_lengths.quantile(0.25):.2f} bytes\")\n",
+    "    print(f\"  - 75th percentile: {packet_lengths.quantile(0.75):.2f} bytes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Packet Length Bins Distribution:\n",
+      "packet_length_bin\n",
+      "0-64            33\n",
+      "64-128        1861\n",
+      "128-256       3513\n",
+      "256-512       7143\n",
+      "512-1024     14168\n",
+      "1024-2048    13282\n",
+      "2048+            0\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Percentage Distribution:\n",
+      "packet_length_bin\n",
+      "0-64          0.08\n",
+      "64-128        4.65\n",
+      "128-256       8.78\n",
+      "256-512      17.86\n",
+      "512-1024     35.42\n",
+      "1024-2048    33.20\n",
+      "2048+         0.00\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create packet length bins\n",
+    "bins_packet = [0, 64, 128, 256, 512, 1024, 2048, float('inf')]\n",
+    "labels_packet = ['0-64', '64-128', '128-256', '256-512', '512-1024', '1024-2048', '2048+']\n",
+    "df['packet_length_bin'] = pd.cut(df['Packet Length'], bins=bins_packet, labels=labels_packet)\n",
+    "\n",
+    "packet_bin_dist = df['packet_length_bin'].value_counts().sort_index()\n",
+    "print(f\"\\nPacket Length Bins Distribution:\")\n",
+    "print(packet_bin_dist)\n",
+    "print(f\"\\nPercentage Distribution:\")\n",
+    "print((packet_bin_dist / packet_bin_dist.sum() * 100).round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize packet length\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "\n",
+    "# Histogram\n",
+    "axes[0, 0].hist(packet_lengths, bins=50, color='skyblue', edgecolor='black', alpha=0.7)\n",
+    "axes[0, 0].set_xlabel('Packet Length (bytes)', fontsize=11)\n",
+    "axes[0, 0].set_ylabel('Frequency (log scale)', fontsize=11)\n",
+    "axes[0, 0].set_title('Packet Length Distribution', fontsize=13, fontweight='bold')\n",
+    "axes[0, 0].set_yscale('log')\n",
+    "axes[0, 0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Binned distribution\n",
+    "packet_bin_dist.plot(kind='bar', ax=axes[0, 1], color='coral', edgecolor='black')\n",
+    "axes[0, 1].set_xlabel('Packet Length Bins (bytes)', fontsize=11)\n",
+    "axes[0, 1].set_ylabel('Count', fontsize=11)\n",
+    "axes[0, 1].set_title('Packet Length Binned Distribution', fontsize=13, fontweight='bold')\n",
+    "axes[0, 1].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "# Box plot by attack type\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    df.boxplot(column='Packet Length', by='Attack Type', ax=axes[1, 0])\n",
+    "    axes[1, 0].set_xlabel('Attack Type', fontsize=11)\n",
+    "    axes[1, 0].set_ylabel('Packet Length (bytes)', fontsize=11)\n",
+    "    axes[1, 0].set_title('Packet Length by Attack Type', fontsize=13, fontweight='bold')\n",
+    "    axes[1, 0].get_figure().suptitle('')  # Remove default title\n",
+    "    plt.sca(axes[1, 0])\n",
+    "    plt.xticks(rotation=45, ha='right')\n",
+    "\n",
+    "# Bins by attack type (stacked bar)\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    bin_attack = pd.crosstab(df['Attack Type'], df['packet_length_bin'], normalize='index') * 100\n",
+    "    bin_attack.plot(kind='bar', stacked=True, ax=axes[1, 1], colormap='tab10')\n",
+    "    axes[1, 1].set_xlabel('Attack Type', fontsize=11)\n",
+    "    axes[1, 1].set_ylabel('Percentage', fontsize=11)\n",
+    "    axes[1, 1].set_title('Packet Length Bins by Attack Type (%)', fontsize=13, fontweight='bold')\n",
+    "    axes[1, 1].legend(title='Packet Size', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "    axes[1, 1].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "PORT USAGE ANALYSIS\n",
+      "================================================================================\n",
+      "\n",
+      "Top 10 Source Ports:\n",
+      "Source Port\n",
+      "41341    6\n",
+      "37135    5\n",
+      "4128     5\n",
+      "16784    5\n",
+      "4779     5\n",
+      "12179    5\n",
+      "34763    5\n",
+      "30175    5\n",
+      "49911    5\n",
+      "4622     5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Top 10 Destination Ports:\n",
+      "Destination Port\n",
+      "34117    6\n",
+      "7508     6\n",
+      "39887    5\n",
+      "59207    5\n",
+      "6871     5\n",
+      "38787    5\n",
+      "19250    5\n",
+      "34873    5\n",
+      "15150    5\n",
+      "12585    5\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2. PORT ANALYSIS\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"PORT USAGE ANALYSIS\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "if 'Source Port' in df.columns and 'Destination Port' in df.columns:\n",
+    "    # Source ports\n",
+    "    print(f\"\\nTop 10 Source Ports:\")\n",
+    "    top_src_ports = df['Source Port'].value_counts().head(10)\n",
+    "    print(top_src_ports)\n",
+    "    \n",
+    "    # Destination ports\n",
+    "    print(f\"\\nTop 10 Destination Ports:\")\n",
+    "    top_dst_ports = df['Destination Port'].value_counts().head(10)\n",
+    "    print(top_dst_ports)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Destination Port Categories:\n",
+      "dst_port_category\n",
+      "Registered (1024-49151)    29926\n",
+      "Dynamic (49152-65535)      10074\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Percentage:\n",
+      "dst_port_category\n",
+      "Registered (1024-49151)    74.82\n",
+      "Dynamic (49152-65535)      25.18\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create port categories\n",
+    "def categorize_port(port):\n",
+    "    \"\"\"Categorize ports into well-known, registered, or dynamic\"\"\"\n",
+    "    if pd.isna(port):\n",
+    "        return 'Unknown'\n",
+    "    try:\n",
+    "        port = int(port)\n",
+    "        if 0 <= port <= 1023:\n",
+    "            return 'Well-known (0-1023)'\n",
+    "        elif 1024 <= port <= 49151:\n",
+    "            return 'Registered (1024-49151)'\n",
+    "        elif 49152 <= port <= 65535:\n",
+    "            return 'Dynamic (49152-65535)'\n",
+    "        else:\n",
+    "            return 'Unknown'\n",
+    "    except:\n",
+    "        return 'Unknown'\n",
+    "\n",
+    "df['dst_port_category'] = df['Destination Port'].apply(categorize_port)\n",
+    "df['src_port_category'] = df['Source Port'].apply(categorize_port)\n",
+    "\n",
+    "print(f\"\\nDestination Port Categories:\")\n",
+    "print(df['dst_port_category'].value_counts())\n",
+    "print(f\"\\nPercentage:\")\n",
+    "print((df['dst_port_category'].value_counts() / len(df) * 100).round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize port analysis\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "\n",
+    "# Top source ports\n",
+    "top_src_ports_15 = df['Source Port'].value_counts().head(15)\n",
+    "axes[0, 0].barh(range(len(top_src_ports_15)), top_src_ports_15.values, color='lightgreen')\n",
+    "axes[0, 0].set_yticks(range(len(top_src_ports_15)))\n",
+    "axes[0, 0].set_yticklabels(top_src_ports_15.index)\n",
+    "axes[0, 0].set_xlabel('Frequency', fontsize=11)\n",
+    "axes[0, 0].set_ylabel('Port Number', fontsize=11)\n",
+    "axes[0, 0].set_title('Top 15 Source Ports', fontsize=13, fontweight='bold')\n",
+    "axes[0, 0].invert_yaxis()\n",
+    "\n",
+    "# Top destination ports\n",
+    "top_dst_ports_15 = df['Destination Port'].value_counts().head(15)\n",
+    "axes[0, 1].barh(range(len(top_dst_ports_15)), top_dst_ports_15.values, color='lightcoral')\n",
+    "axes[0, 1].set_yticks(range(len(top_dst_ports_15)))\n",
+    "axes[0, 1].set_yticklabels(top_dst_ports_15.index)\n",
+    "axes[0, 1].set_xlabel('Frequency', fontsize=11)\n",
+    "axes[0, 1].set_ylabel('Port Number', fontsize=11)\n",
+    "axes[0, 1].set_title('Top 15 Destination Ports', fontsize=13, fontweight='bold')\n",
+    "axes[0, 1].invert_yaxis()\n",
+    "\n",
+    "# Port category pie chart\n",
+    "port_cat_dist = df['dst_port_category'].value_counts()\n",
+    "axes[1, 0].pie(port_cat_dist.values, labels=port_cat_dist.index, autopct='%1.1f%%', startangle=90)\n",
+    "axes[1, 0].set_title('Destination Port Categories', fontsize=13, fontweight='bold')\n",
+    "\n",
+    "# Port categories by attack type\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    port_attack = pd.crosstab(df['Attack Type'], df['dst_port_category'])\n",
+    "    port_attack.plot(kind='bar', stacked=True, ax=axes[1, 1], colormap='Set3')\n",
+    "    axes[1, 1].set_xlabel('Attack Type', fontsize=11)\n",
+    "    axes[1, 1].set_ylabel('Count', fontsize=11)\n",
+    "    axes[1, 1].set_title('Port Categories by Attack Type', fontsize=13, fontweight='bold')\n",
+    "    axes[1, 1].legend(title='Port Category', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "    axes[1, 1].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "PROTOCOL DISTRIBUTION\n",
+      "================================================================================\n",
+      "\n",
+      "Protocol Distribution:\n",
+      "Protocol\n",
+      "ICMP    13429\n",
+      "UDP     13299\n",
+      "TCP     13272\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Percentage:\n",
+      "Protocol\n",
+      "ICMP    33.57\n",
+      "UDP     33.25\n",
+      "TCP     33.18\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3. PROTOCOL ANALYSIS\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"PROTOCOL DISTRIBUTION\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "if 'Protocol' in df.columns:\n",
+    "    protocol_dist = df['Protocol'].value_counts()\n",
+    "    print(f\"\\nProtocol Distribution:\")\n",
+    "    print(protocol_dist)\n",
+    "    print(f\"\\nPercentage:\")\n",
+    "    print((protocol_dist / protocol_dist.sum() * 100).round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Protocol Usage by Attack Type (%):\n",
+      "Protocol      ICMP    TCP    UDP\n",
+      "Attack Type                     \n",
+      "DDoS         33.57  33.05  33.38\n",
+      "Intrusion    33.62  33.15  33.23\n",
+      "Malware      33.52  33.34  33.13\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize protocol analysis\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "\n",
+    "# Protocol pie chart\n",
+    "axes[0].pie(protocol_dist.values, labels=protocol_dist.index, autopct='%1.1f%%', startangle=90)\n",
+    "axes[0].set_title('Protocol Distribution', fontsize=14, fontweight='bold')\n",
+    "\n",
+    "# Protocol by attack type\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    protocol_attack = pd.crosstab(df['Attack Type'], df['Protocol'], normalize='index') * 100\n",
+    "    protocol_attack.plot(kind='bar', stacked=True, ax=axes[1], colormap='viridis')\n",
+    "    axes[1].set_xlabel('Attack Type', fontsize=12)\n",
+    "    axes[1].set_ylabel('Percentage', fontsize=12)\n",
+    "    axes[1].set_title('Protocol Distribution by Attack Type (%)', fontsize=14, fontweight='bold')\n",
+    "    axes[1].legend(title='Protocol', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "    axes[1].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    print(f\"\\nProtocol Usage by Attack Type (%):\")\n",
+    "    print(protocol_attack.round(2))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "ANOMALY SCORES DISTRIBUTION\n",
+      "================================================================================\n",
+      "\n",
+      "Anomaly Score Statistics:\n",
+      "  - Mean: 50.1135\n",
+      "  - Median: 50.3450\n",
+      "  - Std Dev: 28.8536\n",
+      "  - Min: 0.0000\n",
+      "  - Max: 100.0000\n",
+      "  - 25th percentile: 25.1500\n",
+      "  - 50th percentile: 50.3450\n",
+      "  - 75th percentile: 75.0300\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 4. ANOMALY SCORES ANALYSIS\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"ANOMALY SCORES DISTRIBUTION\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "if 'Anomaly Scores' in df.columns:\n",
+    "    anomaly_scores = df['Anomaly Scores'].dropna()\n",
+    "    \n",
+    "    print(f\"\\nAnomaly Score Statistics:\")\n",
+    "    print(f\"  - Mean: {anomaly_scores.mean():.4f}\")\n",
+    "    print(f\"  - Median: {anomaly_scores.median():.4f}\")\n",
+    "    print(f\"  - Std Dev: {anomaly_scores.std():.4f}\")\n",
+    "    print(f\"  - Min: {anomaly_scores.min():.4f}\")\n",
+    "    print(f\"  - Max: {anomaly_scores.max():.4f}\")\n",
+    "    print(f\"  - 25th percentile: {anomaly_scores.quantile(0.25):.4f}\")\n",
+    "    print(f\"  - 50th percentile: {anomaly_scores.quantile(0.50):.4f}\")\n",
+    "    print(f\"  - 75th percentile: {anomaly_scores.quantile(0.75):.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Anomaly Score Categories:\n",
+      "anomaly_category\n",
+      "Low (0-25%)           10004\n",
+      "Medium (25-50%)        9996\n",
+      "High (50-75%)         10001\n",
+      "Critical (75-100%)     9999\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Percentage:\n",
+      "anomaly_category\n",
+      "Low (0-25%)           25.01\n",
+      "Medium (25-50%)       24.99\n",
+      "High (50-75%)         25.00\n",
+      "Critical (75-100%)    25.00\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create anomaly score categories based on quartiles\n",
+    "anomaly_bins = [anomaly_scores.min(), \n",
+    "                anomaly_scores.quantile(0.25),\n",
+    "                anomaly_scores.quantile(0.5),\n",
+    "                anomaly_scores.quantile(0.75),\n",
+    "                anomaly_scores.max()]\n",
+    "anomaly_labels = ['Low (0-25%)', 'Medium (25-50%)', 'High (50-75%)', 'Critical (75-100%)']\n",
+    "\n",
+    "df['anomaly_category'] = pd.cut(df['Anomaly Scores'], bins=anomaly_bins, \n",
+    "                                 labels=anomaly_labels, include_lowest=True)\n",
+    "\n",
+    "anomaly_cat_dist = df['anomaly_category'].value_counts().sort_index()\n",
+    "print(f\"\\nAnomaly Score Categories:\")\n",
+    "print(anomaly_cat_dist)\n",
+    "print(f\"\\nPercentage:\")\n",
+    "print((anomaly_cat_dist / anomaly_cat_dist.sum() * 100).round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize anomaly scores\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "\n",
+    "# Histogram\n",
+    "axes[0, 0].hist(anomaly_scores, bins=50, color='purple', alpha=0.7, edgecolor='black')\n",
+    "axes[0, 0].set_xlabel('Anomaly Score', fontsize=11)\n",
+    "axes[0, 0].set_ylabel('Frequency', fontsize=11)\n",
+    "axes[0, 0].set_title('Anomaly Score Distribution', fontsize=13, fontweight='bold')\n",
+    "axes[0, 0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# Category bar chart\n",
+    "colors = ['green', 'yellow', 'orange', 'red']\n",
+    "axes[0, 1].bar(range(len(anomaly_cat_dist)), anomaly_cat_dist.values, \n",
+    "               color=colors, edgecolor='black')\n",
+    "axes[0, 1].set_xticks(range(len(anomaly_cat_dist)))\n",
+    "axes[0, 1].set_xticklabels(anomaly_cat_dist.index, rotation=45, ha='right')\n",
+    "axes[0, 1].set_xlabel('Anomaly Category', fontsize=11)\n",
+    "axes[0, 1].set_ylabel('Count', fontsize=11)\n",
+    "axes[0, 1].set_title('Anomaly Score Categories', fontsize=13, fontweight='bold')\n",
+    "\n",
+    "# Box plot by attack type\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    df.boxplot(column='Anomaly Scores', by='Attack Type', ax=axes[1, 0])\n",
+    "    axes[1, 0].set_xlabel('Attack Type', fontsize=11)\n",
+    "    axes[1, 0].set_ylabel('Anomaly Score', fontsize=11)\n",
+    "    axes[1, 0].set_title('Anomaly Scores by Attack Type', fontsize=13, fontweight='bold')\n",
+    "    axes[1, 0].get_figure().suptitle('')\n",
+    "    plt.sca(axes[1, 0])\n",
+    "    plt.xticks(rotation=45, ha='right')\n",
+    "\n",
+    "# Category by attack type\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    anomaly_attack = pd.crosstab(df['Attack Type'], df['anomaly_category'], normalize='index') * 100\n",
+    "    anomaly_attack.plot(kind='bar', stacked=True, ax=axes[1, 1], color=colors)\n",
+    "    axes[1, 1].set_xlabel('Attack Type', fontsize=11)\n",
+    "    axes[1, 1].set_ylabel('Percentage', fontsize=11)\n",
+    "    axes[1, 1].set_title('Anomaly Categories by Attack Type (%)', fontsize=13, fontweight='bold')\n",
+    "    axes[1, 1].legend(title='Anomaly Level', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+    "    axes[1, 1].tick_params(axis='x', rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 5. COMPREHENSIVE SUMMARY & INSIGHTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "================================================================================\n",
+      "COMPREHENSIVE EDA SUMMARY\n",
+      "================================================================================\n",
+      "\n",
+      "📊 DATASET OVERVIEW\n",
+      "--------------------------------------------------------------------------------\n",
+      "Total Records: 40,000\n",
+      "Total Features: 35\n",
+      "Memory Usage: 60.68 MB\n",
+      "\n",
+      "🎯 ATTACK TYPE DISTRIBUTION\n",
+      "--------------------------------------------------------------------------------\n",
+      "  DDoS: 13,428 (33.57%)\n",
+      "  Malware: 13,307 (33.27%)\n",
+      "  Intrusion: 13,265 (33.16%)\n",
+      "\n",
+      "🔍 KEY STATISTICS\n",
+      "--------------------------------------------------------------------------------\n",
+      "  - Proxy Usage Rate: 50.37%\n",
+      "  - Unique Source IPs: 40,000\n",
+      "  - Unique Destination IPs: 40,000\n",
+      "  - Average Packet Size: 781.45 bytes\n",
+      "  - Most Common Protocol: ICMP (33.57%)\n",
+      "  - Most Targeted Port: 34117 (6 times)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"COMPREHENSIVE EDA SUMMARY\")\n",
+    "print(\"=\"*80)\n",
+    "\n",
+    "print(f\"\\n📊 DATASET OVERVIEW\")\n",
+    "print(\"-\" * 80)\n",
+    "print(f\"Total Records: {len(df):,}\")\n",
+    "print(f\"Total Features: {df.shape[1]}\")\n",
+    "print(f\"Memory Usage: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB\")\n",
+    "\n",
+    "if 'Attack Type' in df.columns:\n",
+    "    print(f\"\\n🎯 ATTACK TYPE DISTRIBUTION\")\n",
+    "    print(\"-\" * 80)\n",
+    "    attack_dist = df['Attack Type'].value_counts()\n",
+    "    for attack, count in attack_dist.items():\n",
+    "        print(f\"  {attack}: {count:,} ({count/len(df)*100:.2f}%)\")\n",
+    "\n",
+    "print(f\"\\n🔍 KEY STATISTICS\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "# Proxy\n",
+    "if 'has_proxy' in df.columns:\n",
+    "    proxy_pct = (df['has_proxy'].sum() / len(df)) * 100\n",
+    "    print(f\"  - Proxy Usage Rate: {proxy_pct:.2f}%\")\n",
+    "\n",
+    "# IPs\n",
+    "if 'Source IP Address' in df.columns:\n",
+    "    print(f\"  - Unique Source IPs: {df['Source IP Address'].nunique():,}\")\n",
+    "    print(f\"  - Unique Destination IPs: {df['Destination IP Address'].nunique():,}\")\n",
+    "\n",
+    "# Packet Length\n",
+    "if 'Packet Length' in df.columns:\n",
+    "    print(f\"  - Average Packet Size: {df['Packet Length'].mean():.2f} bytes\")\n",
+    "\n",
+    "# Protocol\n",
+    "if 'Protocol' in df.columns:\n",
+    "    top_protocol = df['Protocol'].value_counts().index[0]\n",
+    "    top_protocol_pct = (df['Protocol'].value_counts().values[0] / len(df)) * 100\n",
+    "    print(f\"  - Most Common Protocol: {top_protocol} ({top_protocol_pct:.2f}%)\")\n",
+    "\n",
+    "# Port\n",
+    "if 'Destination Port' in df.columns:\n",
+    "    top_port = df['Destination Port'].value_counts().index[0]\n",
+    "    top_port_count = df['Destination Port'].value_counts().values[0]\n",
+    "    print(f\"  - Most Targeted Port: {top_port} ({top_port_count:,} times)\")\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/script.py b/script.py
index 03c414e..75e1195 100644
--- a/script.py
+++ b/script.py
@@ -31,7 +31,7 @@
 geoip2_doc_url = "https://geoip2.readthedocs.io/en/latest/"
 geoip2_django_doc_url = "https://docs.djangoproject.com/en/5.2/ref/contrib/gis/geoip2/"
 
-# loadding dataset
+# loading dataset
 # df = pd.read_csv( "data/cybersecurity_attacks.csv" )
 df = pd.read_csv( "data/df.csv" , sep = "|" , index_col = 0 )
 
@@ -137,11 +137,15 @@ def crosstab_col( col ,target , name_col , name_target ) :
 
 i = 2
 for destsource in [ "Source" , "Destination" ] :
-    col = df.columns.get_loc( f"{ destsource } IP Address" ) + 1
-    df.insert( col , f"{ destsource } IP latitude" , value = pd.NA )
-    df.insert( col + 1 , f"{ destsource } IP longitude" , value = pd.NA )
-    df.insert( col + 2 , f"{ destsource } IP country" , value = pd.NA )
-    df.insert( col + 3 , f"{ destsource } IP city" , value = pd.NA )
+    col = df.columns.get_loc( f"{ destsource } IP Address" )
+    col_insert = [
+        f"{ destsource } IP latitude" ,
+        f"{ destsource } IP longitude" ,
+        f"{ destsource } IP country" ,
+        f"{ destsource } IP city"
+        ]
+    for col , colnew_name in zip( range( col + 1 , col + len( col_insert ) + 1 ) , col_insert ) :
+        df.insert( col , colnew_name , value = pd.NA )
     df[[ f"{ destsource } IP latitude" , f"{ destsource } IP longitude" , f"{ destsource } IP country" , f"{ destsource } IP city" ]] = df[ f"{ destsource } IP Address" ].apply( lambda x : ip_to_coords( x ))
 
 ## IP address map graph
@@ -206,10 +210,14 @@ def crosstab_col( col ,target , name_col , name_target ) :
 col_name = "Proxy Information"
 # print( df[ col_name ].value_counts())
 col = df.columns.get_loc( col_name )
-df.insert( col + 1 , "Proxy latitude" , value = pd.NA )
-df.insert( col + 2 , "Proxy longitude" , value = pd.NA )
-df.insert( col + 3 , "Proxy country" , value = pd.NA )
-df.insert( col + 4 , "Proxy city" , value = pd.NA )
+col_insert = [ 
+        "Proxy latitude" ,
+        "Proxy longitude" ,
+        "Proxy country" ,
+        "Proxy city"
+        ]
+for col , colnew_name in zip( range( col + 1 , col + len( col_insert ) + 1 ) , col_insert ) :
+    df.insert( col , colnew_name , value = pd.NA )
 df[[ "Proxy latitude" , "Proxy longitude" , "Proxy country" , "Proxy city" ]] = df[ "Proxy Information" ].apply( lambda x : ip_to_coords( x ))
 
 def sankey_diag_IPs( ntop ) :
@@ -406,17 +414,21 @@ def sankey_diag_IPs( ntop ) :
 col_name = "Device Information"
 print( df[ col_name ].value_counts())
 col = df.columns.get_loc( col_name )
-df.insert( col + 1 , "Browser family" , value = pd.NA )
-df.insert( col + 2 , "Browser major" , value = pd.NA )
-df.insert( col + 3 , "Browser minor" , value = pd.NA )
-df.insert( col + 4 , "OS family" , value = pd.NA )
-df.insert( col + 5 , "OS major" , value = pd.NA )
-df.insert( col + 6 , "OS minor" , value = pd.NA )
-df.insert( col + 7 , "Device family" , value = pd.NA )
-df.insert( col + 8 , "Device brand" , value = pd.NA )
-df.insert( col + 9 , "Device type" , value = pd.NA )
-df.insert( col + 10 , "Device bot" , value = pd.NA )
-
+col_insert = [ 
+        "Browser family" ,
+        "Browser major" ,
+        "Browser minor" ,
+        "OS family" ,
+        "OS major" ,
+        "OS minor" ,
+        "OS patch" ,
+        "Device family" ,
+        "Device brand" ,
+        "Device type" ,
+        "Device bot"
+        ]
+for col , colnew_name in zip( range( col + 1 , col + len( col_insert ) + 1 ) , col_insert ) :
+    df.insert( col , colnew_name , value = pd.NA )
 df[ "Browser family" ] = df[ col_name ].apply( lambda x : parse( x ).browser.family if parse( x ).browser.family is not None else pd.NA )
 df[ "Browser major" ] = df[ col_name ].apply( lambda x : parse( x ).browser.version[ 0 ] if parse( x ).browser.version[ 0 ] is not None else pd.NA )
 df[ "Browser minor" ] = df[ col_name ].apply( lambda x: parse( x ).browser.version[ 1 ] if parse( x ).browser.version[ 1 ] is not None else pd.NA )
@@ -426,12 +438,6 @@ def sankey_diag_IPs( ntop ) :
 df[ "OS patch" ] = df[ col_name ].apply( lambda x : parse( x ).os.version[ 2 ] if len( parse( x ).os.version ) > 2 and parse( x ).os.version[ 2 ] is not None else pd.NA )
 df[ "Device family" ] = df[ col_name ].apply (lambda x : parse( x ).device.family if parse( x ).device.family is not None else pd.NA )
 df[ "Device brand" ] = df[ col_name].apply( lambda x : parse( x ).device.brand if parse( x ).device.brand is not None else pd.NA )
-# do not agree with setting to not specified , why nnot leave it pd.NA ??
-# df[ "OS major" ] = df[ "OS_major" ].fillna( "not specified" )
-# df[ "OS minor" ] = df[ "OS_major" ].fillna( "not specified" )
-# df[ "OS patch" ] = df[ "OS_patch" ].fillna( "not specified" )
-# df[ "Device brand" ] = df[ "Device_brand" ].fillna( "not specified" )
-# device info
 def Device_type( ua_string ) :
     try :
         if not ua_string or pd.isna( ua_string ) :
@@ -446,10 +452,15 @@ def Device_type( ua_string ) :
         return pd.NA # replaced "Unknown" with pd.NA
     except :
         return pd.NA # replaced "Unknown" with pd.NA
-df[ "Device type" ] = df[ col_name ].apply( Device_type())
-# detection of bots
+df[ "Device type" ] = df[ col_name ].apply( lambda x : Device_type( x ))
 df[ "Device bot" ] = df[ col_name ].apply( lambda x: ua_parse( x ).is_bot )
 
+
+col_name = "Browser family"
+print( df[ col_name ].value_counts())
+df = catvar_mapping( col_name , [ "Logged" , "Blocked" ])
+piechart_col( col_name )
+
 #%% Network Segment
 col_name = "Network Segment"
 print( df[ col_name ].value_counts())
@@ -512,7 +523,6 @@ def geolocation_data( info ) :
 for attype in attypes :
     df_attype[ attype ] = df[ df[ "Attack Type"] == attype ]
 
-
 #%% geo plot of "Attack Type" by "Anomaly Score"
 
 fig = subp(
@@ -645,6 +655,17 @@ def sankey_diag( cols_bully = [
         True , # "Attack Signature patA"
         True , # "Action Taken"
         True , # "Severity Level"
+        True , # "Browser family"
+        True , # "Browser major"
+        True , # "Browser minor"
+        True , # "OS family"
+        True , # "OS major"
+        True , # "OS minor"
+        True , # "OS patch"
+        True , # "Device family"
+        True , # "Device brand"
+        True , # "Device type"
+        True , # "Device bot"
         True , # "Network Segment"
         True , # "Firewall Logs"
         True , # "IDS/IPS Alerts"
@@ -663,6 +684,17 @@ def sankey_diag( cols_bully = [
         "Attack Signature patA" ,
         "Action Taken" ,
         "Severity Level" ,
+        "Browser family" ,
+        "Browser major" ,
+        "Browser minor" ,
+        "OS family" ,
+        "OS major" ,
+        "OS minor" ,
+        "OS patch" ,
+        "Device family" ,
+        "Device brand" ,
+        "Device type" ,
+        "Device bot" ,
         "Network Segment" ,
         "Firewall Logs" ,
         "IDS/IPS Alerts" ,
@@ -769,17 +801,28 @@ def sankey_diag( cols_bully = [
     False , # "Destination IP country"
     False , # "Source Port ephemeral"
     False , # "Destination Port ephemeral"
-    True , # "Protocol"
+    False , # "Protocol"
     False , # "Packet Type Control"
-    True , # "Traffic Type"
-    True , # "Malware Indicators"
+    False , # "Traffic Type"
+    False , # "Malware Indicators"
     False , # "Alert Trigger"
     False , # "Attack Signature patA"
     False , # "Action Taken"
     False , # "Severity Level"
+    True , # "Browser family"
+    False , # "Browser major"
+    False , # "Browser minor"
+    True , # "OS family"
+    False , # "OS major"
+    False , # "OS minor"
+    False , # "OS patch"
+    False , # "Device family"
+    False , # "Device brand"
+    False , # "Device type"
+    False , # "Device bot"
     False , # "Network Segment"
     False , # "Firewall Logs"
-    True , # "IDS/IPS Alerts"
+    False , # "IDS/IPS Alerts"
     False , # "Log Source Firewall"
     ])
 
@@ -800,6 +843,17 @@ def paracat_diag( cols_bully = [
         True , # "Attack Signature patA"
         True , # "Action Taken"
         True , # "Severity Level"
+        True , # "Browser family"
+        True , # "Browser major"
+        True , # "Browser minor"
+        True , # "OS family"
+        True , # "OS major"
+        True , # "OS minor"
+        True , # "OS patch"
+        True , # "Device family"
+        True , # "Device brand"
+        True , # "Device type"
+        True , # "Device bot"
         True , # "Network Segment"
         True , # "Firewall Logs"
         True , # "IDS/IPS Alerts"
@@ -822,6 +876,17 @@ def paracat_diag( cols_bully = [
         "Attack Signature patA" ,
         "Action Taken" ,
         "Severity Level" ,
+        "Browser family" ,
+        "Browser major" ,
+        "Browser minor" ,
+        "OS family" ,
+        "OS major" ,
+        "OS minor" ,
+        "OS patch" ,
+        "Device family" ,
+        "Device brand" ,
+        "Device type" ,
+        "Device bot" ,
         "Network Segment" ,
         "Firewall Logs" ,
         "IDS/IPS Alerts" ,
@@ -914,18 +979,29 @@ def paracat_diag( cols_bully = [
     False , # "Destination Port ephemeral"
     True , # "Protocol"
     False , # "Packet Type Control"
-    True , # "Traffic Type"
+    False , # "Traffic Type"
     False , # "Malware Indicators"
     False , # "Alert Trigger"
-    True , # "Attack Signature patA"
+    False , # "Attack Signature patA"
     False , # "Action Taken"
-    True , # "Severity Level"
-    True , # "Network Segment"
+    False , # "Severity Level"
+    True , # "Browser family"
+    False , # "Browser major"
+    False , # "Browser minor"
+    True , # "OS family"
+    False , # "OS major"
+    False , # "OS minor"
+    False , # "OS patch"
+    False , # "Device family"
+    False , # "Device brand"
+    False , # "Device type"
+    False , # "Device bot"
+    False , # "Network Segment"
     False , # "Firewall Logs"
-    False , # "IDS/IPS Alerts"
+    True , # "IDS/IPS Alerts"
     False , # "Log Source Firewall"
     ] ,
-    colorvar = "Attack Signature patA" ,
+    colorvar = "Browser family" ,
     )
 
 
@@ -1116,78 +1192,3 @@ def catvar_corr( col , target = "Attack Type") :
 df.to_csv( "data/df.csv" , sep = "|" )
 
 
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-#%%
-
-rw = 30
-
-tr1 = go.Scatter(
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "Malware" ].rolling( rw ).mean() ,
-    line_color = "blue" ,
-)
-tr2 = go.Scatter(
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "Intrusion" ].rolling( rw ).mean() ,
-    line_color = "red" ,
-    # yaxis = "y2"
-)
-tr3 = go.Scatter(
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "DDoS" ].rolling( rw ).mean() ,
-    line_color = "#000000" ,
-    # yaxis = "y2" 
-)
-
-fig = subp()
-fig.add_trace(tr1)
-fig.add_trace(tr2)
-fig.add_trace(tr3)
-fig.show()
-
-#%%
-
-px.line(
-    Attacks_pday ,
-    x = Attacks_pday.index ,
-    y = Attacks_pday[ "DDoS" ].rolling( 15 ).mean() ,
-    mode = "line" ,
-    title = "Number of DDoS attacks MA per day"
-)
-
-#%%
-Attacks_pmonth = df.copy( deep = True )
-Attacks_pmonth[ "date_mm" ] = Attacks_pmonth[ "date" ].dt.ceil( "d" )
-Attacks_pmonth = Attacks_pmonth.groupby([ "date_mm" , "Attack Type" ]).size().unstack().iloc[ 1 : - 1 , ]
-px.line(
-    Attacks_pmonth ,
-    x = Attacks_pmonth.index ,
-    y = "DDoS" ,
-    title = "Number of DDoS attacks per month"
-)
-
-
-
-