Malware Classification with Deep Learning
Overview
A deep learning model for classifying malware samples into 31 different families using convolutional neural networks (CNN). The project demonstrates the application of computer vision techniques to cybersecurity, achieving over 93% accuracy on malware classification.
Technical Details
- Built using TensorFlow and Keras
- CNN architecture with multiple convolutional and pooling layers
- Dataset of 13,747 malware samples across 31 malware families
- Image-based approach treating malware binaries as visual patterns
- Hardware-accelerated training using GPU
Key Features
- Data preprocessing and augmentation pipeline
- Custom model architecture optimized for malware patterns
- Comprehensive training metrics and visualizations
- Confusion matrix analysis for classification performance
- Cross-validation to ensure model robustness
Technical Capabilities
Feature Extraction
Static and dynamic analysis with PE file parsing
Classification Engine
Ensemble model with multiple detection techniques
Analysis Pipeline
Automated sample processing with sandbox integration
Results
- 93.97% accuracy on test dataset
- Successfully classified 31 different malware families
- Training convergence in 10 epochs
- Effective handling of class imbalance
- Low false positive rate across malware families
Technical Implementation
The model uses a sequential CNN architecture with:
- Multiple convolutional layers with increasing filter counts (16, 32, 64)
- Max pooling layers for feature extraction
- Dense layers for final classification
- Adam optimizer and categorical cross-entropy loss
- Data augmentation and preprocessing for improved generalization