dc.contributor
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor
Universitat Politècnica de Catalunya. Departament d'Enginyeria Minera, Industrial i TIC
dc.contributor
Universitat Politècnica de Catalunya. CRAAX - Centre de Recerca d'Arquitectures Avançades de Xarxes
dc.contributor
Universitat Politècnica de Catalunya. CROMAI - Computing Resources Orchestration and Management for AI
dc.contributor.author
Pou Freixas, Albert
dc.contributor.author
Otero Calviño, Beatriz
dc.contributor.author
Canal Corretger, Ramon
dc.date.accessioned
2026-03-27T10:48:32Z
dc.date.available
2026-03-27T10:48:32Z
dc.date.issued
2026-01-26
dc.identifier
Pou, A.; Otero, B.; Canal, R. Detecting hardware attacks in RISC-V with on-chip performance counters. 2026.
dc.identifier
https://hdl.handle.net/2117/459589
dc.identifier.uri
https://hdl.handle.net/2117/459589
dc.description.abstract
Poster presentado en HiPEAC 2026 (Cracovia)
dc.description.abstract
Malware is evolving faster than traditional defenses. Signature-based tools often miss new polymorphic and metamorphic variants, leaving modern systems exposed. Embedded platforms add further constraints, demanding lightweight and autonomous protection. Processor Hardware Performance Counters (HPCs) reveal microarchitectural footprints of malicious activity. When combined with Machine Learning, they enable real-time, low-overhead malware detection—even on resource-constrained devices. Across five evaluated ML models, a Random Forest trained on five key HPCs achieves over 99.5% accuracy and detects nearly all unseen attacks, demonstrating strong precision and generalization.
dc.description.abstract
Funded by the European Union. Project number: 101093062.
dc.description.abstract
Preprint
dc.format
application/pdf
dc.relation
info:eu-repo/grantAgreement/EC/HE/101093062/EU/Virtual Environment and Tool-boxing for Trustworthy Development of RISC-V based Cloud Services/Vitamin-V
dc.subject
Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica
dc.subject
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject
Machine learning
dc.subject
Hardware Performance Counters
dc.title
Detecting hardware attacks in RISC-V with on-chip performance counters
dc.type
External research report
