Feature Importance Guided Attack: A Model Agnostic Adversarial Attack
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Machine learning models are susceptible to adversarial attacks which dramatically reduce their performance. Reliable defenses to these attacks are an unsolved challenge. In this work, we present a novel evasion attack: the 'Feature Importance Guided Attack' (FIGA) which generates adversarial evasion samples. FIGA is model agnostic, it assumes no prior knowledge of the defending model's learning algorithm, but does assume knowledge of the feature representation. FIGA leverages feature importance rankings; it perturbs the most important features of the input in the direction of the target class we wish to mimic. We demonstrate FIGA against eight phishing detection models. We keep the attack realistic by perturbing phishing website features that an adversary would have control over. Using FIGA we are able to cause a reduction in the F1-score of a phishing detection model from 0.96 to 0.41 on average. Finally, we implement adversarial training as a defense against FIGA and show that while it is sometimes effective, it can be evaded by changing the parameters of FIGA.
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