Simple single-feature perturbations, such as shifted timestamps and altered values, sharply reduce the accuracy of Random Forest, Decision Tree, and KNN Ethereum phishing detectors, with adversarial training reported to restore performance.
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Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations
Simple single-feature perturbations, such as shifted timestamps and altered values, sharply reduce the accuracy of Random Forest, Decision Tree, and KNN Ethereum phishing detectors, with adversarial training reported to restore performance.