SCAGuard: Detection and Classification of Cache Side-Channel Attacks via Attack Behavior Modeling and Similarity Comparison
Limin Wang, Lei Bu, Fu Song · 2023
Cache side-channel attacks (CSCAs), capable of deducing secrets by analyzing timing differences in the shared cache behavior of modern processors, pose a serious security threat. While there are approaches for detecting CSCAs and mitigating information leaks, they either fail to detect and classify new variants or have to impractically update deployed systems (e.g., CPU). In this work, we propose a novel approach, named SCAGuard, to detect and classify CSCAs via attack behavior modeling and similarity comparison. Specifically, we introduce the notion of cache state transition enhanced basic block sequences (CST-BBSes) to model attack behaviors which is able to capture both attack-relevant syntactic code information and semantic cache information. We propose an approach to automatically construct CST-BBS models from binary programs. To detect and classify attacks, we adapt a dynamic time warping algorithm to compare the similarity of CST-BBSes between attack and target programs. We implement our approach in a tool SCAGuard and evaluate it using real-world attacks and diverse benign programs. The results confirm the effectiveness of our approach, compared over existing detection approaches. In particular, SCAGuard significantly outperforms the other detection approaches on new variants.