A Bitcoin Transaction Network Analytic Method for Future Blockchain Forensic Investigation

Popular Blockchain-based cryptocurrencies, like Bitcoin, are increasingly being used maliciously for illegal trades. In order to trace and analyze suspected Bitcoin transactions addresses, address clustering methods flow analysis gaining attention recently. However, existing only focus on addresses flow, neglect other important information, such as transaction structure behavior features. exploit all useful features of transactions, this paper proposes a network analytic method facilitating Blockchain forensic investigation based an extended safe Petri Net. The structural dynamic semantics net in our proposed model define the static transactions. Nineteen have been identified patterns analyzing finding addresses. gene has embedded into transitions accurately. Finally, marginal distribution data visualization techniques eliminate some false positive samples further improve accuracy identifying provides reliable along with prototype platform which is beneficial financial security. efficiency empirically verified real-life case study analysis.