Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk

Author:

Yan Chuyi,Zhang Chen,Shen Meng,Li Ning,Liu Jinhao,Qi Yinhao,Lu Zhigang,Liu Yuling

Abstract

AbstractEthereum’s high attention, rich business, certain anonymity, and untraceability have attracted a group of attackers. Cybercrime on it has become increasingly rampant, among which scam behavior is convenient, cryptic, antagonistic and resulting in large economic losses. So we consider the scam behavior on Ethereum and investigate it at the node interaction level. Based on the life cycle and risk identification points we found, we propose an automatic detection model named Aparecium. First, a graph generation method which focus on the scam life cycle is adopted to mitigate the sparsity of the scam behaviors. Second, the life cycle patterns are delicate modeled because of the crypticity and antagonism of Ethereum scam behaviors. Conducting experiments in the wild Ethereum datasets, we prove Aparecium is effective which the precision, recall and F1-score achieve at 0.977, 0.957 and 0.967 respectively.

Funder

National Key Research and Development Program of China

Youth Innovation Promotion Association CAS

Strategic Priority Research Program of Chinese Academy of Sciences

National Natural Science Foundation of China

Program of Key Laboratory of Network Assessment Technology

Chinese Academy of Sciences, Program of Beijing Key Laboratory of Network Security and Protection Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Information Systems,Software

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