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.

翻译：随着以太坊上各种形式的欺诈行为不断蔓延，必须防御这些恶意活动以保护易受攻击的用户免受侵害。虽然当前研究仅依赖基于图的欺诈检测方法，但本文认为这些方法可能不适用于处理高度重复、分布偏斜且异构的以太坊交易。为解决这些问题，我们提出了BERT4ETH，一种通用的预训练Transformer编码器，作为账户表示提取器来检测以太坊上的各种欺诈行为。BERT4ETH利用Transformer卓越的建模能力捕获以太坊交易中固有的动态序列模式，并通过三种实用且有效的策略（即重复性降低、偏斜缓解和异构性建模）解决了为以太坊预训练BERT模型的挑战。我们的实证评估表明，在钓鱼账户检测和去匿名化任务中，BERT4ETH以显著优势优于现有最先进方法。BERT4ETH的代码可在以下网址获取：https://github.com/git-disl/BERT4ETH。