This project aims to design a malware detection system capable of identifying polymorphic malware that frequently changes its code structure. The system analyzes structural patterns and opcode sequences instead of relying solely on fixed signatures.
Study polymorphic and metamorphic malware techniques.
Research opcode analysis and code pattern recognition methods.
Collect malware samples demonstrating polymorphic behavior.
Extract opcode sequences or byte-level patterns from executable files.
Implement feature extraction methods focusing on structural similarities.
Apply machine learning models to classify malware families.
Compare detection rates with traditional signature-based systems.
Develop visualization tools to show pattern similarities.
Test robustness against modified malware samples.
Analyze false positives and improve detection logic.
Document findings and research implications.
