基于交易时间衰减的以太坊恶意地址检测方法OA

This article proposes the Trans-TAN model for detecting malicious addresses associated with transaction flow graphs on Ethereum.The model improves the self attention mechanism based on the Transformer model.Inspired by Newton's cooling theo-rem and based on the transaction characteristics of Ethereum addresses,the Trans-TAN model introduces the time decay factor of transaction intervals over time.At the same time,it integrates the similarity factor between Ethereum addresses and the transac-tion amount factor.Based on the above three factors,the address association matrix is constructed in the form of a solution to the ordinary differential equation of Newton's cooling theorem,thereby improving the original self attention matrix.Experimental re-sults show that the Trans-TAN model can effectively capture the characteristics of malicious node addresses in the Ethereum trans-action flow graph process,and its accuracy in the test set(Pre)is improved.The precision,recall,and F1 metrics are superior to traditional detection models.