Chapter 19 — TorTracker
Based On: SIH1745 — Dark Web Investigation & De-anonymization Tool (NCIIPC)
Skills Required:
- Network traffic analysis & correlation
- Tor and darknet understanding
- Anonymity & de-anonymization techniques
- Machine learning (anomaly detection)
- OSINT and digital investigation
- Python programming and scripting
- Big data analytics & graph processing
Project Description: TorTracker is an advanced investigation toolkit designed to analyze Tor hidden services, detect user activity patterns, and identify potential deanonymization opportunities. Leveraging traffic correlation, behavioral analysis, and machine learning, the tool assists law enforcement agencies in uncovering illicit dark web networks. The solution integrates darknet crawling, traffic capture, and graph analytics to map and track suspect entities.
Tech Stack:
- Python (data parsing, ML modeling, scripts)
- Tor network client & traffic capture tools (e.g., Wireshark, TShark)
- Network graph databases (Neo4j)
- Machine learning frameworks (Scikit-learn, TensorFlow)
- Data visualization (D3.js, Kibana)
- Scraping tools for dark web forums and marketplaces
Week-wise Plan:
Week 1 — Darknet Network Familiarization & Tool Setup
- Study Tor protocol, hidden services structure, and anonymity threats.
- Setup Tor client with monitoring tools, capture traffic in controlled environment.
- Deploy Neo4j and data ingestion pipelines.
- Deliverable: Darknet dev environment and initial data capture tooling.
Week 2 — Darknet Crawling & Data Extraction
- Develop crawlers to scrape marketplaces, forums, and hidden services.
- Extract metadata, user profiles, communication patterns.
- Store crawled data into graph DB.
- Deliverable: Crawling engine and populated darknet dataset.
Week 3 — Traffic Correlation & Flow Analysis
- Capture Tor traffic patterns, anonymize captured metadata.
- Analyze timing, volume, and packet signatures for correlation opportunities.
- Develop flow linkage algorithms.
- Deliverable: Traffic correlation module with initial analysis reports.
Week 4 — Behavioral Pattern Modeling
- Extract activity features (posting frequency, transaction timings).
- Train ML models for anomaly/outlier detection in user activity.
- Identify potential deanonymization vectors.
- Deliverable: Behavioral analytics engine with anomaly detection.
Week 5 — Entity Resolution & Network Mapping
- Integrate metadata and traffic analysis results to identify entity clusters.
- Map interconnections to construct suspect networks.
- Develop visualization dashboards.
- Deliverable: Network mapping module and interactive graphs.
Week 6 — Investigation Workflow & Reporting
- Design investigation workflow integrating alerting and evidence tagging.
- Generate automated reports for law enforcement use.
- Deliverable: Investigation management console and report templates.
Week 7 — Real-time Monitoring & Alerts
- Implement real-time darknet monitoring with threshold-based alerts.
- Integrate notification channels (email, SMS).
- Deliverable: Live monitoring dashboard and alerting system.
Week 8 — Testing, Validation & Documentation
- Simulate attack/deanonymization scenarios in testbed.
- Evaluate detection accuracy, false positive rates.
- Prepare comprehensive user and developer docs.
- Deliverable: Validated toolset with training materials.
Testing & Deliverables:
- Controlled darknet crawling & traffic capture sessions.
- Validation against known Tor hidden service behavior.
- Final deliverables include full source code, documentation, demonstrative datasets, and video demonstrations.