| Title | Identification of Darknet Markets’ Bitcoin Addresses by Voting Per-address Classification Results |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Kanemura, Kota, Toyoda, Kentaroh, Ohtsuki, Tomoaki |
| Conference Name | 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) |
| Keywords | bitcoin, bitcoin security, Bitcoin transactions, blockchain, classification, common ledger, cryptocurrencies, darknet markets, data privacy, decentralized digital currency, Distributed databases, DNM, drugs, feature extraction, financial data processing, forensic, Human Behavior, illegal products, law enforcement, machine learning, Measurement, pubcrawl, Roads, Scalability, security forensics aspect, voting per-address classification, Weapons |
| Abstract | Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification. |
| DOI | 10.1109/BLOC.2019.8751391 |
| Citation Key | kanemura_identification_2019 |
Identification of Darknet Markets’ Bitcoin Addresses by Voting Per-address Classification Results