BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

Abstract

As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.

The ACM Web conference (WWW) 2023

References in corpus (4)

• TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection
• Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach
• Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges
• Identity Inference on Blockchain using Graph Neural Network

Cited by in corpus (1)

• Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision