Annual Computer Security Applications Conference (ACSAC) 2023

TGC: Transaction Graph Contrast Network for Ethereum Phishing Scam Detection

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.

Sijia Li
Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of the Chinese Academy of Sciences

Gaopeng Gou
Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of the Chinese Academy of Sciences

Chang Liu
Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of the Chinese Academy of Sciences

Gang Xiong
Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of the Chinese Academy of Sciences

Zhen Li
Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of the Chinese Academy of Sciences

Junchao Xiao
Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of the Chinese Academy of Sciences

Xinyu Xing
Northwestern University

Paper (ACM DL)

Slides