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. PM - Programming Models
dc.contributor
Universitat Politècnica de Catalunya. CROMAI - Computing Resources Orchestration and Management for AI
dc.contributor.author
Costa Prats, Juan José
dc.contributor.author
Morancho Llena, Enrique
dc.contributor.author
Canal Corretger, Ramon
dc.contributor.author
Otero Calviño, Beatriz
dc.date.accessioned
2026-03-27T12:37:12Z
dc.date.available
2026-03-27T12:37:12Z
dc.date.issued
2026-01-27
dc.identifier
Costa, J. [et al.]. Detecting RISC-V hardware attacks using instruction opcodes with QEMU. 2026.
dc.identifier
https://hdl.handle.net/2117/459876
dc.identifier.uri
https://hdl.handle.net/2117/459876
dc.description.abstract
Póster presentado en HiPEAC 2026 (Cracovia)
dc.description.abstract
As malware becomes more advanced, there is a growing need for more accurate and robust detection systems. Artificial Intelligence (AI) techniques, particularly those based on machine learning and deep learning, have emerged as powerful tools for identifying and classifying malicious behavior. Can we detect RISC-V Hardware Attacks using a machine learning model analyzing sequences of executed instructions? This work builds a suitable dataset, trains machine learning models and evaluates models in 2 scenarios: known and zero-day attacks, showing that known attacks can be detected with a high accuracy, while zero-day attacks may be detected but depends on the similarity with other trained attacks.
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.title
Detecting RISC-V hardware attacks using instruction opcodes with QEMU
dc.type
External research report
