Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations

• URL: http://arxiv.org/abs/2504.17684v1
• Date: Thu, 24 Apr 2025 15:54:56 GMT
• Title: Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations
• Authors: Ahod Alghuried, Ali Alkinoon, Abdulaziz Alghamdi, Soohyeon Choi, Manar Mohaisen, David Mohaisen,
• Abstract summary: This paper explores the vulnerability of machine learning models to simple single-feature adversarial attacks in the context of fraudulent transaction detection.<n>Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics.<n>We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness and show their effectiveness.
• Score: 9.362363409064546
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper explores the vulnerability of machine learning models to simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness and show their effectiveness.

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