2024/08/06 by Ahod Alghureid, Alghureid, Ahod, Aziz Mohaisen +1
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.2408.03441
openalex publication_date 2024/08/06 · openalex created_date 2024/11/01 · openalex updated_date 2026/07/28
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.