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Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis

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Information Security Applications (WISA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15499))

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Abstract

This paper explores the vulnerability of machine learning models, specifically Random Forest, Decision Tree, and K-Nearest Neighbors, to very 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, such as accuracy, precision, recall, and F1-score. 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.

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Authors and Affiliations

  1. University of Central Florida, Orlando, USA

    Ahod Alghureid & David Mohaisen

Authors
  1. Ahod Alghureid
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  2. David Mohaisen
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Correspondence to Ahod Alghureid .

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Editors and Affiliations

  1. Sejong University, Seoul, Korea (Republic of)

    Jong-Hyouk Lee

  2. Kanazawa University, Kanazawa, Japan

    Keita Emura

  3. Gachon University, Seongnam, Korea (Republic of)

    Sokjoon Lee

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Alghureid, A., Mohaisen, D. (2025). Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis. In: Lee, JH., Emura, K., Lee, S. (eds) Information Security Applications. WISA 2024. Lecture Notes in Computer Science, vol 15499. Springer, Singapore. https://doi.org/10.1007/978-981-96-1624-4_10

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  • DOI: https://doi.org/10.1007/978-981-96-1624-4_10

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