FriendlyFoe: Adversarial Machine Learning as a Practical Architectural Defense against Side Channel Attacks
Year: 2024
Type: conference-paper
Abstract: Machine learning (ML)-based side channel attacks have become prominent threats to computer security. These attacks are often powerful, as ML models easily find patterns in signals. To address this problem, this paper proposes dynamically applying Adversarial Machine Learning (AML) to obfuscate side channels. The rationale is that it has been shown that intelligently injecting an adversarial perturbation can confuse ML classifiers. We call this approach FriendlyFoe and the neural network we introduce to perturb signals FriendlyFoe Defender.
Institutions: University of Illinois Urbana-Champaign, Yale University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403334280