• Background Bitcoin's pseudonymous, peer-to-peer design lets criminal actors move, layer, and cash out illicit funds — ransomware payments, darknet-market proceeds, extortion, and laundering — while evading traditional financial surveillance.
The objective of problem statement is to design and build a complete system (offline) that ingests bulk Bitcoin transaction/network metadata (in CSV/JSON/XML), correlates network-layer (IP/port/timing) observations with blockchain-layer (wallet/TXID/amount) data, and applies AI/ML to detect anomalies, cluster entities, and generate prioritized, explainable investigative leads.
• Description i.Challenge Objectives- • Ingest & parse a bulk metadata dataset (timestamp, src/dst IP & port, TXID, input/output wallet addresses, amounts, fee, script type).
• Build an entity/transaction graph linking IPs, wallets, and transactions.
• Implement AI/ML detection use case (see Section 4) with a working model — not just rules.
• Generate a ranked, explainable alert list (why a wallet/transaction was flagged, with a confidence score).
• Present findings via a simple dashboard or link-analysis visualization.
ii.Suggested AI/ML Focus Areas Attach Table Here of AI/ML Focus Areas iii.Dataset: Parameters & Synthetic Generation Participants will work with a synthetic dataset modelled on real Bitcoin P2P/transaction fields (no real seized or live-intercept data will be provided). Minimum fields: timestamp, src_ip, dst_ip, src_port, dst_port, txid, input_addresses[], output_addresses[], input_amounts[], output_amounts[], geo_country/asn (integrate open source downloadable Geo IP database).
• Expected Solution • Workable complete offline solution for linux platform.
• Working prototype (code repo) with ingestion, correlation, and AI/ML model.
• Short technical write-up: approach, model choice, and explain ability method.
• Dashboard/visualization showing flagged entities and evidence for each flag.