Introduction
This lab addresses the detection of polymorphic malware using generative AI models.
Objectives
- Understand polymorphic malware and its challenges in detection.
- Explore how generative AI can simulate and detect polymorphic malware.
- Test the classifier's performance against synthetic polymorphic malware.
Lab Steps
1. Generate Polymorphic Malware:
- Use the GAN to create synthetic malware variants.
2. Train and Test the Model:
- Train the classification network on the malware dataset.
- Test the model against synthetic malware.
3. Evaluate Robustness:
- Analyze how well the model detects unseen variants.
- Measure metrics like recall and F1-score for adversarial samples.
4. Discussion:
- Discuss how generative AI improves or threatens malware detection.