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Botnet detection based on machine learning have witnessed significant leapsin recent years, with the availability of large and reliable datasets that areextracted from real-life scenarios. Consequently, adversarial attacks onmachine learning-based cybersecurity systems are posing a significant threat tothe practicality of these solutions. In this paper, we introduce a novel attackthat utilizes machine learning model's explainability in evading detection bybotnet detection systems. The proposed attack utilizes information obtainedfrom model's explainability to build adversarial samples that can evadedetection in a blackbox setting. The proposed attack was tested on a trainedIoT botnet detection systems and was capable of bypassing the botnet detectionwith 0% detection by altering one feature only to generate the adversarialsamples.

Adversarial Explainability: Utilizing Explainable Machine Learning in Bypassing IoT Botnet Detection Systems