in progress · Contributor · March 2025
AI-Based Vulnerability Detection System
A collaborative security prototype that combines static code analysis with behavioral analysis to identify potential vulnerabilities from source code and network activity.
- Python
- Django
- React
- TypeScript
- Machine Learning
- Cybersecurity
- Static Analysis
- Behavioral Analysis
Built with the PICT Cyber Cell, this system explores how multiple forms of analysis can work together to surface vulnerabilities that a single detection method might miss.
Two analysis paths
- Static code analysis accepts a compressed codebase for analysis without executing it.
- Behavioral analysis combines a related codebase with PCAP network-capture data to examine runtime and network behavior.
- A combined workflow brings both sources together to support a more complete vulnerability report.
Engineering approach
- Developed an AI-oriented backend with Django and Python for vulnerability-analysis workflows.
- Worked with the CICIDS2017 network-intrusion dataset and preprocessing notebooks for behavioral analysis.
- Used trained CNN and Random Forest model artifacts as part of the network-analysis pipeline.
- Built a React and TypeScript interface for uploading code and PCAP inputs and presenting analysis results.
The project is collaborative and remains an evolving prototype rather than a production security scanner.