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

Cybersecurity

Research

Chuyi Yan¹  Yuling Liu¹  Jinhao Liu¹  Ning Li¹  Zhigang Lu¹  Chen Zhang¹  Yinhao Qi¹  Meng Shen² 

[1] Institute of Information Engineering, Chinese Academy of Sciences, 100093, Beijing, China;School of Cyber Security, University of Chinese Academy of Sciences, 100049, Beijing, China;School of Cyberspace Science and Technology, Beijing Institute of Technology, 100081, Beijing, China;

关键词: Blockchain;    Network security;    Ethereum;    Scam detection;    Behavior understanding;   

DOI  :  10.1186/s42400-023-00180-x

 received in 2023-05-24, accepted in 2023-07-13,  发布年份 2023

来源: Springer

【 摘 要 】

Ethereum’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.

【 授权许可】

CC BY   
© The Author(s) 2023

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