Chapter 25 — VulnHunter
Based on: SIH1676 — Automated Vulnerability Intelligence Gathering Platform
Organization: National Technical Research Organisation (NTRO)
Skills Required
- Web scraping and data extraction
- Natural Language Processing (NLP) for threat intelligence
- Vulnerability databases (CVE, NVD)
- Python scripting for automation
- Data analysis and correlation
- API development
- Elasticsearch or similar for indexing and search
Project Description
VulnHunter is an automated vulnerability intelligence gathering platform that continuously scans multiple sources—OEM websites, security advisories, vulnerability feeds—and extracts actionable vulnerability data. It leverages advanced scraping techniques and NLP to parse unstructured advisories, standardize vulnerability records, and prioritize critical exposures for further investigation. The system supports APIs for integration with SIEM and incident response tools.
Tech Stack
- Python for scraping, NLP, and orchestration
- BeautifulSoup, Scrapy for scraping
- NLP libraries: spaCy, NLTK, Transformers
- Elasticsearch for indexing and search
- FastAPI or Flask for RESTful APIs
- MongoDB or PostgreSQL for supplementary data storage
Week-wise Plan
Week 1 — Requirements Gathering and Source Identification
- Identify key vulnerability data sources including OEM sites, NVD, security bulletins.
- Define capture scope and parsing targets.
- Develop scraping framework base.
- Deliverable: Source catalog and initial scraping scripts.
Week 2 — Automated Web Scraping and Data Extraction
- Build crawlers for target sites with scheduling and proxy rotation.
- Extract structured and unstructured data fields.
- Implement error handling and duplicate filtering.
- Deliverable: Stable scraping modules for major sources.
Week 3 — NLP for Unstructured Advisory Parsing
- Develop text processing pipelines for parsing advisories.
- Extract CVE IDs, affected components, severity, and mitigation info.
- Normalize data into standardized templates.
- Deliverable: NLP parser integrated into data pipeline.
Week 4 — Data Correlation and Enrichment
- Link scraped data with existing vulnerability databases.
- Enrich records with contextual intelligence such as exploit availability.
- Enable tagging and scoring of vulnerabilities.
- Deliverable: Correlation engine and enriched dataset.
Week 5 — Indexing and Search Engine Integration
- Setup Elasticsearch cluster for indexing scraped and processed vulnerabilities.
- Develop search API with filtering and ranking capabilities.
- Deliverable: Searchable vulnerability intelligence platform.
Week 6 — API and Integration
- Create RESTful APIs for querying and ingesting data.
- Enable integration points for SIEM, ticketing and incident response workflows.
- Deliverable: Production-grade API server.
Week 7 — Dashboard and Alerting
- Build user-friendly dashboard for visualization of vulnerability trends and alerts.
- Implement custom alerts and subscription management.
- Deliverable: Interactive dashboard and notification system.
Week 8 — Testing, Documentation, and Deployment
- Test scraping coverage, element extraction accuracy, and system scalability.
- Document architecture, API usage, and deployment processes.
- Deploy Dockerized platform with CI/CD pipelines.
- Deliverable: Complete VulnHunter platform ready for operational deployment.
Testing and Deliverables
- Evaluate scraping effectiveness across target sites with ongoing monitoring.
- Validate NLP precision and recall on advisory datasets.
- Produce system documentation, demo videos, and source code repository.
VulnHunter empowers security teams with continuous visibility into emerging vulnerabilities, enabling proactive risk management and faster response.