{"ID":2852259,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18612","arxiv_id":"2510.18612","title":"DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.18612","url_pdf":"https://arxiv.org/pdf/2510.18612v1","authors":"[\"Muhammad Hassan\",\"Maria Mushtaq\",\"Jaan Raik\",\"Tara Ghasempouri\"]","published":"2025-10-21T13:09:56Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AR\"]","methods":"[]","has_code":false}