Chapter 32 — ThreatMap AI
Based on: SIH1680 — Cyber Threat Intelligence and Attack Surface Mapping Using AI
Skills Required
- AI/Machine learning for cybersecurity
- Threat intelligence analysis
- Network mapping and visualization
- Python, TensorFlow/PyTorch
- Big data handling and analytics
- Web development for dashboards (React, D3.js)
- API development and integration
Project Description
ThreatMap AI is an AI-enabled cyber threat intelligence platform designed to visualize and analyze the attack surface dynamically. By correlating multiple threat intelligence feeds with organizational network data, it uses machine learning models to predict potential attack paths, identify vulnerable assets, and prioritize defense efforts. The platform features real-time dashboards, risk scoring, and integration with existing security tools.
Tech Stack
- Python and ML libraries (TensorFlow, scikit-learn)
- Network scanning tools (Nmap)
- Threat intelligence APIs (AlienVault, VirusTotal, etc.)
- Graph database (Neo4j)
- Frontend frameworks (React, D3.js)
- FastAPI or Flask backend
- Elasticsearch for search and analytics
Week-wise Roadmap
Week 1 — Dataset Collection & Requirement Analysis
- Collect threat intelligence feeds and network topology data.
- Define attack surface mapping metrics and AI model goals.
- Setup development environment and repositories.
- Deliverable: Requirement specification and dataset catalog.
Week 2 — Network Discovery and Data Ingestion
- Develop modules for asset discovery and network scanning.
- Build pipelines for ingesting and normalizing diverse threat feeds.
- Deliverable: Data ingestion and network mapping prototype.
Week 3 — Feature Engineering & Correlation
- Extract features from network and threat data for AI modeling.
- Implement correlation engine linking threats to assets.
- Deliverable: Correlation module with sample outputs.
Week 4 — ML Model Development
- Train machine learning models to predict attack paths and vulnerable nodes.
- Evaluate model accuracy and adjust hyperparameters.
- Deliverable: Trained AI models and evaluation reports.
Week 5 — Visualization Dashboard
- Build interactive dashboards to visualize attack surface, threat levels, and AI predictions.
- Integrate drill-down and filtering capabilities.
- Deliverable: Dashboard prototype.
Week 6 — Alerting and Integration
- Develop alerting mechanisms for detected high-risk attack paths.
- Integrate with SIEM/SOAR for automated response workflows.
- Deliverable: Alerting system and integration API.
Week 7 — Testing and Optimization
- Conduct system tests with simulated attacks and real data.
- Optimize system for performance and scalability.
- Deliverable: Test reports and performance improvements.
Week 8 — Documentation and Deployment
- Prepare user manuals, developer documentation, and deployment guides.
- Deploy containerized solution for easy adoption.
- Deliverable: Complete ThreatMap AI platform.
Testing and Deliverables
- Validate predictions and visualizations on sample enterprise networks and threat scenarios.
- Provide source code, trained models, dashboards, and demo videos.
ThreatMap AI offers organizations a data-driven perspective on their threat landscape, enabling proactive security planning and resource allocation.