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Spectre intrusions exploit speculative execution design vulnerabilities inmodern processors. The attacks violate the principles of isolation in programsto gain unauthorized private user information. Current state-of-the-artdetection techniques utilize micro-architectural features or vulnerablespeculative code to detect these threats. However, these techniques areinsufficient as Spectre attacks have proven to be more stealthy with recentlydiscovered variants that bypass current mitigation mechanisms. Side-channelsgenerate distinct patterns in processor cache, and sensitive informationleakage is dependent on source code vulnerable to Spectre attacks, where anadversary uses these vulnerabilities, such as branch prediction, which causes adata breach. Previous studies predominantly approach the detection of Spectreattacks using the microarchitectural analysis, a reactive approach. Hence, inthis paper, we present the first comprehensive evaluation of static andmicroarchitectural analysis-assisted machine learning approaches to detectSpectre vulnerable code snippets (preventive) and Spectre attacks (reactive).We evaluate the performance trade-offs in employing classifiers for detectingSpectre vulnerabilities and attacks.

Short Paper: Static and Microarchitectural ML-Based Approaches For Detecting Spectre Vulnerabilities and Attacks