Cybersecurity threats continue to grow in complexity, making intelligent intrusion detection systems essential for protecting modern networks. This project presents a Machine Learning and Deep Learning-based Intrusion Detection System (IDS) designed to identify malicious network activities and classify network traffic as either normal or attack traffic.
The project utilizes advanced data preprocessing, feature engineering, Principal Component Analysis (PCA), multiple Machine Learning algorithms, and Deep Neural Networks to enhance cybersecurity threat detection and network monitoring.
- Network Intrusion Detection System (IDS)
- Binary Classification of Network Traffic
- Principal Component Analysis (PCA)
- Machine Learning Model Comparison
- Deep Learning-Based Threat Detection
- Data Visualization and Performance Analysis
- Cybersecurity Analytics
This project uses the NSL-KDD dataset, a benchmark dataset widely used in intrusion detection and cybersecurity research.
- Network Traffic Records
- Normal and Attack Classes
- Multiple Network Features
- Cybersecurity Benchmark Dataset
- Suitable for Machine Learning and Deep Learning Applications
- Python
- Scikit-Learn
- XGBoost
- TensorFlow
- Keras
- NumPy
- Pandas
- Matplotlib
- Seaborn
The following preprocessing techniques were applied:
- Data Cleaning
- Feature Engineering
- Label Encoding
- RobustScaler Normalization
- Train-Test Split
- Data Transformation
PCA was applied to reduce dimensionality and improve computational efficiency while retaining the most important information from the dataset.
Benefits include:
- Reduced Feature Space
- Faster Training Time
- Improved Model Generalization
- Reduced Noise and Redundancy
The project evaluates multiple Machine Learning algorithms:
Linear classification model for intrusion detection.
Distance-based classification algorithm.
Probabilistic classifier based on Bayes' theorem.
Margin-based classification model.
Tree-based attack classification model.
Ensemble learning model using multiple decision trees.
Advanced gradient boosting framework for high-performance predictive modeling.
The Deep Learning architecture includes:
- Dense Layers
- ReLU Activation Functions
- Dropout Layers
- Binary Output Layer
The model is designed to learn complex patterns in network traffic and improve attack detection performance.
- Data Collection
- Data Preprocessing
- Feature Engineering
- PCA Feature Reduction
- Train-Test Split
- Machine Learning Model Training
- Deep Learning Model Training
- Performance Evaluation
- Model Comparison
The models were evaluated using:
- Accuracy
- Precision
- Recall
- F1 Score
- Mean Squared Error (MSE)
- Confusion Matrix
The project includes:
- Class Distribution Analysis
- Feature Importance Analysis
- PCA Visualization
- Decision Tree Visualization
- Training Accuracy Curves
- Validation Accuracy Curves
- Training Loss Curves
- Model Performance Comparison
- Network Security Monitoring
- Cyber Threat Detection
- Security Operations Centers (SOC)
- Enterprise Cybersecurity Systems
- Anomaly Detection Systems
- Intelligent Network Defense
- Real-Time Intrusion Detection
- Explainable AI (XAI) for Cybersecurity
- Cloud-Based Deployment
- Zero-Day Attack Detection
- Advanced Deep Learning Architectures
- Federated Learning for Cybersecurity
intrusion-detection-using-ml-and-dl/
├── Intrusion_Detection_System.ipynb
├── README.md
├── requirements.txt
│
├── images/
│ ├── pca_visualization.png
│ ├── confusion_matrix.png
│ ├── model_comparison.png
│ └── training_curves.png
│
└── dataset/
Machine Learning Researcher | Embedded Systems Researcher
- Artificial Intelligence
- Deep Learning
- Explainable AI
- Cybersecurity Analytics
- Computer Vision
- Network Security
GitHub: https://github.com/AnkurRay25
This project is licensed under the MIT License.