CTDM: cryptocurrency abnormal transaction detection method with spatio-temporal and global representation

With the rapid advances in computing and networking technologies, there have led to the creation of a novel and booming set of payment services, known as cryptocurrencies or digital tokens. Many are available for exchanges worldwide, inviting investors to trade with costs, quality, and safety that vary widely. Nevertheless, Blockchain transaction data have complex time and space dependencies, and historical transaction data reflect the transaction trends of cryptocurrencies to a certain extent, thus identifying the illegal behaviors of transactions such as money laundering more at the earliest. In this article, we propose a novel cryptocurrency abnormal transaction detection method with spatio-temporal and global representation, namely CTDM. CTDM combines EvolveGCN with MGU and global representations to achieve better performance. In addition, CTDM needs fewer learning parameters through MGU, which leads to less training time. Experimental results show that the proposed CTDM method outperforms SOTA Blockchain abnormal transaction detection methods.

Automated summary

With the advancements in computing and networking technologies, there has been a rise in cryptocurrencies or digital tokens, offering various payment services with different costs, quality, and safety. Analyzing blockchain transaction data and historical transaction trends can help identify illegal behaviors, such as money laundering, at an early stage. In this article, a novel method called CTDM is proposed, which combines EvolveGCN with MGU and global representations to achieve better performance in abnormal transaction detection compared to existing methods.