A time-aware LSTM model for detecting criminal activities in blockchain transactions
Akhila Reddy Yadulla, Mounica Yenugula, Vinay Kumar Kasula, Bhargavi Konda, Santosh Reddy Addula, Sarath Babu Rakki
Abstract
This paper introduces the Time-Aware LSTM (T-LSTM) model to identify criminal activities involving USDT on wallet addresses within the blockchain ecosystem. The model utilizes a time-aware LSTM architecture to learn the continuous variations in node address features over different transaction time intervals. Additionally, a gating mechanism filters the influence intensity of neighboring transaction node addresses on the central node. The gating mechanism accounts for the transactional correlation strength between node addresses. Finally, a self-attention mechanism is employed to integrate node address features across various transaction timestamps, producing a comprehensive feature representation for the addresses. Experimental results demonstrate that the T-LSTM model effectively captures the dynamic feature changes of node addresses over irregular transaction intervals, outperforming traditional detection models regarding precision, recall, and F1 score on the test set.