Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation. (arXiv:2302.01474v1 [cs.CR])

Side-channel attacks that use machine learning (ML) for signal analysis have
become prominent threats to computer security, as ML models easily find
patterns in signals. To address this problem, this paper explores using
Adversarial Machine Learning (AML) methods as a defense at the computer
architecture layer to obfuscate side channels. We call this approach Defensive
ML, and the generator to obfuscate signals, defender. Defensive ML is a
workflow to design, implement, train, and deploy defenders for different
environments. First, we …

address adversarial aml analysis architecture attacks call channel computer computer security defender defense defensive design find generator machine machine learning ml models obfuscation patterns problem security side-channel side-channel attacks signal signals threats