Malware continues to be one of the most significant cybersecurity threats affecting individuals, organizations, and critical infrastructure. Traditional signature-based detection systems often struggle to identify newly emerging malware variants.
This project presents a Machine Learning-based Malware Detection System that classifies executable files as either legitimate or malicious using Portable Executable (PE) file features. Multiple machine learning algorithms are evaluated and compared to identify the most effective approach for malware detection.
- Malware vs Legitimate File Classification
- PE File Feature Analysis
- Data Preprocessing and Feature Engineering
- Class Imbalance Handling using SMOTE
- Multiple Machine Learning Models
- Performance Comparison and Evaluation
- Cybersecurity Analytics
The project utilizes a malware analysis dataset containing Portable Executable (PE) file characteristics.
- PE Header Information
- Linker Information
- File Structure Attributes
- Executable Metadata
- Malware Labels
| Value | Class |
|---|---|
| 0 | Malware |
| 1 | Legitimate |
The following preprocessing techniques were applied:
- Missing Value Handling
- Feature Selection
- Label Encoding
- Data Cleaning
- Class Balancing using SMOTE
- Feature Scaling using StandardScaler
Synthetic Minority Oversampling Technique (SMOTE) was used to address class imbalance and improve model performance.
The following classification algorithms were implemented and evaluated:
Ensemble learning model based on multiple decision trees.
Gradient boosting framework optimized for high-performance classification.
Tree-based classification algorithm for malware detection.
Linear classification model for binary prediction.
Margin-based classification model for malware classification.
Distance-based classification algorithm.
Dataset Collection
│
▼
Data Cleaning
│
▼
Feature Engineering
│
▼
SMOTE Balancing
│
▼
Feature Scaling
│
▼
Model Training
│
▼
Model Evaluation
│
▼
Performance Comparison
| Model | Accuracy |
|---|---|
| Random Forest | 99.21% |
| XGBoost | 99.17% |
| Decision Tree | 98.64% |
| KNN | 98.05% |
| SVM | 96.44% |
| Logistic Regression | 92.37% |
Random Forest Classifier achieved the highest accuracy of 99.21%, demonstrating excellent capability for malware detection.
The models were evaluated using:
- Accuracy
- Precision
- Recall
- F1-Score
- Confusion Matrix
- Classification Report
- Python
- Scikit-Learn
- XGBoost
- Pandas
- NumPy
- Matplotlib
- Seaborn
- SMOTE (Imbalanced-Learn)
The experimental results demonstrate that machine learning techniques can effectively distinguish malicious software from legitimate applications using PE file characteristics.
The comparison of multiple classification algorithms highlights the effectiveness of ensemble learning methods such as Random Forest and XGBoost for cybersecurity applications.
- Malware Detection Systems
- Threat Intelligence Platforms
- Endpoint Security Solutions
- Security Operations Centers (SOC)
- Cybersecurity Analytics
- Malware Classification
- Machine Learning Security Applications
- Threat Detection Research
malware-detection-using-machine-learning/
├── Malware_Detection_Using_Machine_Learning.ipynb
├── README.md
├── requirements.txt
│
├── images/
│ ├── class_distribution.png
│ ├── confusion_matrix.png
│ ├── model_comparison.png
│ └── feature_analysis.png
│
└── dataset/
- Python 3.x
- Pandas
- NumPy
- Scikit-Learn
- XGBoost
- Imbalanced-Learn
- Matplotlib
- Seaborn
- Deep Learning-based Malware Detection
- Explainable AI (XAI) for Cybersecurity
- Real-Time Malware Detection Systems
- Feature Importance Analysis
- Zero-Day Malware Detection
- Cloud-Based Threat Detection
Machine Learning Researcher | Embedded Systems Researcher
- Artificial Intelligence
- Machine Learning
- Cybersecurity
- Deep Learning
- Explainable AI
- Threat Detection
GitHub: https://github.com/AnkurRay25
This project is licensed under the MIT License.