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