Evasion Attacks Against Behavioral Biometric Continuous Authentication Using a Generative Adversarial Network
Abstract: The aim of the project was to examine the feasibilityof evading continuous authentication systems with a generativeadversarial network. To this end, a group of supervised andunsupervised state-of-the-art classifiers were trained on a publiclyavailable dataset of stroke patterns on mobile devices. To find thebest configurations for each classifier, hyper-parameter searcheswere performed. To attack the classifiers, a generative adversarialnetwork was trained on the dataset to reproduce samples followingthe same distribution. The generative adversarial networkwas optimized to maximize the Equal Error Rate metric of theclassifiers on the reproduced data. Our results show that theEqual Error Rate and the Threshold False Acceptance Rateincreased on generated samples compared to random evasionattacks. Across the classifiers, the greatest increase in Equal ErrorRate was 26 percent (for the artificial neural network), and thegreatest increase in Threshold False Acceptance Rate was 60percent for the same classifier. Moreover, it was found that, ingeneral, the unsupervised classifiers were more robust towardsthis type of attack. The results indicate that evasion attacksagainst continuous authentication systems using a generativeadversarial network are feasible and thus constitute a real threat.
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