通过对比交易特征与图结构特征，提升以太坊钓鱼攻击检测精度。
Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions
- 结合显式交易特征与隐式图结构特征进行检测
- 发现图模型在对抗环境下更稳定，准确率更高
- 适合关注区块链安全与模型鲁棒性的研究者
以太坊上的钓鱼检测越来越多地采用先进机器学习技术识别欺诈性交易。然而，关于特征选择策略的有效性以及图模型在提升检测准确率中的作用，仍缺乏深入研究。本文系统分析并对比了显式交易特征与隐式图结构特征，从实验与理论两方面考察其对钓鱼检测模型性能的影响，尤其关注以太坊交易网络中的特征表现。同时，针对类别不平衡和数据集构成等关键挑战，评估其对检测方法稳健性与精确度的影响。结果表明，不同特征类型各有优劣，为理解特征如何影响模型在对抗环境下的韧性与泛化能力提供了更清晰的视角。
原文摘要 · Abstract (English)
Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detection accuracy. In this paper, we systematically examine these issues by analyzing and contrasting explicit transactional features and implicit graph-based features, both experimentally and analytically. We explore how different feature sets impact the performance of phishing detection models, particularly in the context of Ethereum's transactional network. Additionally, we address key challenges such as class imbalance and dataset composition and their influence on the robustness and precision of detection methods. Our findings demonstrate the advantages and limitations of each feature type, while also providing a clearer understanding of how feature affect model resilience and generalization in adversarial environments.
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