  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.
