Anti-Laundering Approach for Bitcoin Transactions

Money laundering has significantly increased in recent years as a result of the quick growth of financial systems and the emergence of cryptocurrencies. These crimes threaten the stability of both economic and social systems and impact the security of public and private financial institutions. In response, detecting and preventing money laundering has become a top priority for financial systems, which rely on deep analysis of assets and the nature of funds to uncover illegitimate sources. With the rise of cryptocurrencies like Bitcoin, criminals have adopted new technologies and channels to cover up their illegal activities. This research aims to develop a new deep learning architecture for detecting illegitimate Bitcoin transactions in order to prevent Bitcoin laundering crimes. In this study, a deep investigation of the previous works will be conducted for better understanding of the problem statement. Besides, a new framework will be designed to exceed the performance of current best practices with regard to accuracy. Ultimately, the findings of this research will aid the financial sector in combating Bitcoin laundering activities and strengthening the security of digital financial transactions. An elliptic dataset used for bitcoin transactions that belong to real entities, where it consists of 203,769 nodes and 234,355 edges for illicit and licit transactions. The licit transaction families are licit services, exchanges, miners, and wallet providers. While the illicit transaction families are malware, frauds, Ponzi (fraud) schemes, ransomware, and terrorist organizations. The proposed model semi-supervised generative adversarial network (SGAN) utilized to complete the labelling process of the unknown entities. Based on the experimental results, the proposed model significantly improved upon previous methods, achieving a 98% success rate in accurately predicting illicit transactions. Thus, we consider it as a competitive stand in terms of anti-laundering for the Bitcoin cryptocurrency.