| Title | Forensic Analysis of Bitcoin Transactions |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Wu, Yan, Luo, Anthony, Xu, Dianxiang |
| Conference Name | 2019 IEEE International Conference on Intelligence and Security Informatics (ISI) |
| Date Published | jul |
| Keywords | bitcoin, bitcoin security, bitcoin transaction data, bitcoin transaction net, Bitcoin transactions, Computer science, cryptocurrencies, data mining, data privacy, FABT, financial data processing, Firing, forensic analysis, forensic investigation, Forensics, Human Behavior, Pattern matching, Petri nets, pubcrawl, Scalability, suspicious bitcoin addresses, transaction patterns, transaction processing, visualization |
| Abstract | Bitcoin [1] as a popular digital currency has been a target of theft and other illegal activities. Key to the forensic investigation is to identify bitcoin addresses involved in bitcoin transfers. This paper presents a framework, FABT, for forensic analysis of bitcoin transactions by identifying suspicious bitcoin addresses. It formalizes the clues of a given case as transaction patterns defined over a comprehensive set of features. FABT converts the bitcoin transaction data into a formal model, called Bitcoin Transaction Net (BTN). The traverse of all bitcoin transactions in the order of their occurrences is captured by the firing sequence of all transitions in the BTN. We have applied FABT to identify suspicious addresses in the Mt.Gox case. A subgroup of the suspicious addresses has been found to share many characteristics about the received/transferred amount, number of transactions, and time intervals. |
| DOI | 10.1109/ISI.2019.8823498 |
| Citation Key | wu_forensic_2019 |
Forensic Analysis of Bitcoin Transactions