Increasing the Robustness of a Machine Learning-based IoT Malware Detection Method with Adversarial Training
József Sándor, Roland Nagy, Levente Buttyán · 2023
We study the robustness of SIMBIoTA-ML, a recently proposed machine learning-based IoT malware detection solution against adversarial samples. First, we propose two adversarial sample creation strategies that modify existing malware binaries by appending extra bytes to them such that those extra bytes are never executed, but they make the modified samples dissimilar to the original ones. We show that SIMBIoTA-ML is robust against the first strategy, but it can be misled by the second one. To overcome this problem, we propose to use adversarial training, i.e., to extend the training set of SIMBIoTA-ML with samples that are crafted by using the adversarial evasion strategies. We measure the detection accuracy of SIMBIoTA-ML trained on such an extended training set and show that it remains high both for the original malware samples and for the adversarial samples.