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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number46
JournalCybersecurity
Volume6
Issue number1
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Behavior understanding
  • Blockchain
  • Ethereum
  • Network security
  • Scam detection

Fingerprint

Dive into the research topics of 'Aparecium: understanding and detecting scam behaviors on Ethereum via biased random walk'. Together they form a unique fingerprint.

Cite this