DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining

• URL: http://arxiv.org/abs/2510.18612v1
• Date: Tue, 21 Oct 2025 13:09:56 GMT
• Title: DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining
• Abstract summary: Detection of microarchitectural attacks in RISC-V is relatively underexplored, compared to x86 and ARM.<n>We propose a new detection method combining statistical preprocessing and association rule mining.<n>The proposed detection method achieves up to 5.15% increase in accuracy, 7% rise in precision, and 3.91% improvement in recall.
• Score: 1.6013674661312771
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: RISC-V processors are becoming ubiquitous in critical applications, but their susceptibility to microarchitectural side-channel attacks is a serious concern. Detection of microarchitectural attacks in RISC-V is an emerging research topic that is relatively underexplored, compared to x86 and ARM. The first line of work to detect flush+fault-based microarchitectural attacks in RISC-V leverages Machine Learning (ML) models, yet it leaves several practical aspects that need further investigation. To address overlooked issues, we leveraged gem5 and propose a new detection method combining statistical preprocessing and association rule mining having reconfiguration capabilities to generalize the detection method for any microarchitectural attack. The performance comparison with state-of-the-art reveals that the proposed detection method achieves up to 5.15% increase in accuracy, 7% rise in precision, and 3.91% improvement in recall under the cryptographic, computational, and memory-intensive workloads alongside its flexibility to detect new variant of flush+fault attack. Moreover, as the attack detection relies on association rules, their human-interpretable nature provides deep insight to understand microarchitectural behavior during the execution of attack and benign applications.

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