Active Learning Based Adversary Evasion Attacks Defense for Malwares in the Internet of Things

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.

Automated summary

In this article, the authors studied adversarial evasion attacks in an active learning environment and proposed a feature subset selection method to prevent evasion attacks in IoT environments. They trained an independent classification model for individual Android applications by extracting application-specific data. By comparing and evaluating Android malware benchmarks using ensemble-based active learning, followed by the use of a collaborative machine learning classifier, they demonstrated protection against adversarial evasion attacks. The proposed approach achieved a receiver operating characteristic of 0.91 with 14 fabricated input features.