DeepCloak: Adversarial Crafting As a Defensive Measure to Cloak Processes

Over the past decade, side-channels have proven to be significant and practical threats to modern computing systems. Recent attacks have all exploited the underlying shared hardware. While practical, mounting such a complicated attack is still akin to listening on a private conversation in a crowded train station. The attacker has to either perform significant manual labor or use AI systems to automate the process. The recent academic literature points to the latter option. With the abundance of cheap computing power and the improvements made in AI, it is quite advantageous to automate such tasks. By using AI systems however, malicious parties also inherit their weaknesses, most notably the vulnerability to adversarial samples. In this work, we propose the use of adversarial learning as a defensive tool to obfuscate and mask side-channel information. We demonstrate the viability of this approach by first training CNNs and other machine learning classifiers on leakage trace of different processes. After training a highly accurate model (99+% accuracy), we test it against adversarial learning. We show that through minimal perturbations to input traces, the defender can run as an attachment to the original process and cloak it against a malicious classifier. Finally, we investigate if an attacker can use adversarial defense methods, adversarial re-training and defensive distillation to protect the model. Our results show that even in the presence of an intelligent adversary that employs such techniques, adversarial learning methods still manage to successfully craft perturbations hence the proposed cloaking methodology succeeds.