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arXiv · 1902.04416

Examining Adversarial Learning against Graph-based IoT Malware Detection Systems

Ahmed Abusnaina·Aminollah Khormali·Hisham Alasmary·Jeman Park·Afsah Anwar·Ulku Meteriz·Aziz Mohaisen
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Abstract

The main goal of this study is to investigate the robustness of graph-based Deep Learning (DL) models used for Internet of Things (IoT) malware classification against Adversarial Learning (AL). We designed two approaches to craft adversarial IoT software, including Off-the-Shelf Adversarial Attack (OSAA) methods, using six different AL attack approaches, and Graph Embedding and Augmentation (GEA). The GEA approach aims to preserve the functionality and practicality of the generated adversarial sample through a careful embedding of a benign sample to a malicious one. Our evaluations demonstrate that OSAAs are able to achieve a misclassification rate (MR) of 100%. Moreover, we observed that the GEA approach is able to misclassify all IoT malware samples as benign.

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Ahmed Abusnaina, Aminollah Khormali, Hisham Alasmary, Jeman Park, Afsah Anwar, Ulku Meteriz, Aziz Mohaisen. 2019-02-12. Examining Adversarial Learning against Graph-based IoT Malware Detection Systems. https://arxiv.org/abs/1902.04416

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