Abstract

In this article, we study adversarial evasion attacks in the context of an active learning environment. To prevent evasion attacks in Internet of Things environments, a feature subset selection method is proposed. To train an independent classification model for a single Android application, the approach extracts application-specific data from that application. We compare and evaluate the performance of Android malware benchmarks using ensemble-based active learning, followed by the use of a collaborative machine learning classifier to protect against adversarial evasion attacks on a dataset of Android malware benchmarks. It was found that the proposed approach generates 0.91 receiver operating characteristic with 14 fabricated input features.

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