  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.
