Chapter 27 — CryptoTracer
Based on: SIH1675 — Blockchain Forensics: Identify End Receiver of Cryptocurrency Transaction
Organization: Narcotics Control Bureau (NCB)
Skills Required
- Blockchain fundamentals (Bitcoin, Ethereum, others)
- Graph database and analytics (Neo4j)
- Blockchain data extraction tools (RPC, parity, geth)
- Machine learning for pattern analysis
- Python programming
- Data visualization and UI development
- Cryptocurrency transaction tracing and AML (Anti-Money Laundering) techniques
Project Description
CryptoTracer is a blockchain forensic system designed to analyze cryptocurrency transaction networks to trace and identify the ultimate end recipient of funds. It uses graph analytics, heuristics like common-input clustering and change address detection, and machine learning to cluster addresses and detect suspicious patterns. The platform supports case management, risk scoring of transactions, and reporting for law enforcement and financial analytics.
Tech Stack
- Python (data extraction, analytics)
- Neo4j graph database for transaction graph storage and querying
- Blockchain node clients (Bitcoin Core, Ethereum Geth/Parity)
- ML libraries (scikit-learn, TensorFlow)
- Flask/FastAPI (backend API)
- React.js (frontend interface)
Week-wise Plan
Week 1 — Blockchain Node Setup & Data Extraction
- Install and synchronize blockchain nodes for Bitcoin and Ethereum.
- Build data extractors to parse transactions, blocks, and address metadata.
- Deliverable: Transaction dataset ingestion pipeline.
Week 2 — Graph Database Modeling
- Design graph schema to store addresses, transactions, clusters, and relations.
- Populate Neo4j with extracted transaction data.
- Deliverable: Initial transaction graph database.
Week 3 — Clustering Heuristics Implementation
- Implement common-input clustering, change address detection, and entity linking.
- Validate clusters against known address labels (exchanges, mixers).
- Deliverable: Address clustering engine.
Week 4 — Graph Traversal & Risk Scoring
- Develop algorithms to traverse transaction paths and propagate risk scores.
- Integrate known threat intel and blacklists to score entities.
- Deliverable: Risk scoring and traversal module.
Week 5 — Machine Learning for Anomaly Detection
- Extract features from transaction flows for ML modeling.
- Train classifiers to detect atypical transaction patterns.
- Deliverable: ML anomaly detection engine.
Week 6 — Case Management & UI Development
- Build case workbench for investigators to view and annotate clusters.
- Develop React frontend to display graph visualizations and reports.
- Deliverable: Investigation UI and workflow tool.
Week 7 — Reporting and API Services
- Implement template-based report generation (PDF, HTML).
- Develop APIs for external systems to query case and risk data.
- Deliverable: Reporting system and API backend.
Week 8 — Testing, Validation & Documentation
- Test system on historical blockchain events and public datasets.
- Validate cluster accuracy and risk scoring performance.
- Document setup, usage, and developer guidelines.
- Deliverable: Production-ready CryptoTracer system.
Testing and Deliverables
- Validate detection against labeled blockchain datasets.
- Create demonstration cases simulating forensic investigations.
- Deliver source code repo, setup scripts, documentation, and video tutorials.
CryptoTracer equips law enforcement with powerful tools to unravel complex cryptocurrency transactions, enhancing investigation outcomes and financial crime mitigation.