Sökning: onr:"swepub:oai:research.chalmers.se:db157b57-bc50-4246-9d60-e5c0c9c49af3" >
Machine Learning-Ba...
Machine Learning-Based Classification of Hardware Trojans in FPGAs Implementing RISC-V Cores
-
- Ribes, Stefano, 1992 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
-
- Malatesta, Fabio (författare)
- Università degli Studi di Siena,University of Siena
-
- Garzo, Grazia (författare)
- Università degli Studi di Siena,University of Siena
-
visa fler...
-
Palumbo, Alessandro (författare)
-
visa färre...
-
(creator_code:org_t)
- 2024
- 2024
- Engelska.
-
Ingår i: International Conference on Information Systems Security and Privacy. - 2184-4356. ; 1, s. 717-724
- Relaterad länk:
-
https://research.cha... (primary) (free)
-
visa fler...
-
https://doi.org/10.5...
-
https://research.cha...
-
visa färre...
Abstract
Ämnesord
Stäng
- Hardware Trojans (HTs) pose a severe threat to integrated circuits, potentially compromising electronic devices, exposing sensitive data, or inducing malfunction. Detecting such malicious modifications is particularly challenging in complex systems and commercial CPUs, where they can occur at various design stages, from initial HDL coding to the final hardware implementation. This paper introduces a machine learningbased strategy for the detection and classification of HTs within RISC-V soft cores implemented in FieldProgrammable Gate Arrays (FPGAs). Our approach comprises a systematic methodology for comprehensive data collection and estimation from FPGA bitstreams, enabling us to extract insights ranging from hardware performance counters to intricate metrics like design clock frequency and power consumption. Our ML models achieve perfect accuracy scores when analyzing features related to both synthesis, implementation results, and performance counters. We also address the challenge of identifying HTs solely through performance counters, highlighting the limitations of this approach. Additionally, our work emphasizes the significance of Implementation Features (IFs), particularly circuit timing, in achieving high accuracy in HT detection.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- Feature Importance
- RISC-V
- Machine Learning
- Hardware Security
- Hardware Trojans
- FPGA
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas