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

作　　者：Chuyi Yan Chen Zhang Meng Shen Ning Li Jinhao Liu Yinhao Qi Zhigang Lu Yuling Liu 

机构地区：[1]Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China [2]School of Cyber Security,University of Chinese Academy of Sciences,Beijing 100049,China [3]School of Cyberspace Science and Technology,Bejing Institute of Technology,Beijing 100081,China

出　　处：《Cybersecurity》2024年第3期16-31,共16页网络空间安全科学与技术（英文）

基　　金：This research is supported by National Key Research and Development Program of China(No.2021YFF0307203,No.2019QY1300);Youth Innovation Promotion Association CAS(No.2021156);the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDC02040100);National Natural Science Foundation of China(No.61802404)。

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.

关 键 词：Blockchain Network security Ethereum Scam detection Behavior understanding 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]