TGC: Transaction Graph Contrast Network for Ethereum Phishing Scam Detection

Sijia Li, Gaopeng Gou, Chang Liu, Gang Xiong, Zhen Li, Junchao Xiao, Xinyu Xing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Phishing scams have become the most serious type of crime involved in Ethereum. However, existing methods ignore the natural camouflage and sparse distribution of phishing scams in Ethereum leading to unsatisfactory performance, and they are also limited by the data scale which cannot be applied to real-world dynamic scenarios. In this paper, we propose a Transaction Graph Contrast network (TGC) to enhance phishing scam detection performance on Ethereum. TGC inputs subgraphs instead of the entire graph for training, which eases the model's requirements for machine configuration and data connectivity. Motivated by phishing nodes are surrounded by normal nodes, we design the comparison between node-level to help phishing nodes learn the unique properties of themselves different from their neighbors. Observing the small number and sparse distribution of phishing nodes, we narrow the distance between phishing nodes by comparing node context-level structures, so as to learn universal transaction patterns. We further combine the obtained features with common statistics to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TGC outperforms the state-of-the-art methods in detecting phishing addresses and has obvious advantages in large-scale and dynamic scenarios.

Original languageEnglish (US)
Title of host publicationProceedings - 39th Annual Computer Security Applications Conference, ACSAC 2023
PublisherAssociation for Computing Machinery
Pages352-365
Number of pages14
ISBN (Electronic)9798400708862
DOIs
StatePublished - Dec 4 2023
Event39th Annual Computer Security Applications Conference, ACSAC 2023 - Austin, United States
Duration: Dec 4 2023Dec 8 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference39th Annual Computer Security Applications Conference, ACSAC 2023
Country/TerritoryUnited States
CityAustin
Period12/4/2312/8/23

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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