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Is your FPGA bitstream Hardware Trojan-free? Machine learning can provide an answer

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carlosiii.embargo.liftdate2024-07-01
carlosiii.embargo.terms2024-07-01
dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorPalumbo, Alessandro
dc.contributor.authorCassano, Luca
dc.contributor.authorLuzzi, Bruno
dc.contributor.authorHernández Gutiérrez, José Alberto
dc.contributor.authorReviriego Vasallo, Pedro
dc.contributor.authorBianchi, Giuseppe
dc.contributor.authorOttavi, Marco
dc.contributor.funderComunidad de Madrides
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.date.accessioned2022-10-20T08:13:04Z
dc.date.issued2022-07-01
dc.description.abstractSoftware exploitable Hardware Trojan Horses (HTHs) inserted into commercial CPUs allow the attacker to run his/her own software or to gain unauthorized privileges. Recently a novel menace raised: HTHs inserted by CAD tools. A consequence of such scenario is that HTHs must be considered a serious threat not only by academy but also by industry. In this paper we try to answer to the following question: can Machine Learning (ML) help designers of microprocessor softcores implemented onto SRAM-based FPGAs at detecting HTHs introduced by the employed CAD tool during the generation of the bitstream? We present a comparative analysis of the ability of several ML models at detecting the presence of HTHs in the bitstream by exploiting a previously performed characterization of the microprocessor softcore and an associated ML training. An experimental analysis has been carried out targeting the IBEX RISC-V microprocessor running a set of benchmark programs. A detailed comparison of multiple ML models is conducted, showing that many of them achieve accuracy above 98%, and kappa values above 0.97. By identifying the most effective ML models and the best features to be employed, this paper lays the foundation for the integration of a ML-based bitstream verification flow.en
dc.description.sponsorshipJ. A. Hernández and P. Reviriego acknowledge the ACHILLES PID2019-104207RB-I00 and 6G-INTEGRATION-3-TSI-063000-2021-127 projects and the Go2Edge RED2018-102585-T network funded by the Spanish Agencia Estatal de Investigación (AEI) 10.13039/501100011033 and the Madrid Community research project TAPIR-CM grant no. P2018/TCS-4496.en
dc.format.extent11
dc.identifier.bibliographicCitationPalumbo, A., Cassano, L., Luzzi, B., Hernández, J. A., Reviriego, P., Bianchi, G. & Ottavi, M. (2022, julio). Is your FPGA bitstream Hardware Trojan-free? Machine learning can provide an answer. Journal of Systems Architecture, 128, 102543.en
dc.identifier.doihttps://doi.org/10.1016/j.sysarc.2022.102543
dc.identifier.issn1383-7621
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage11
dc.identifier.publicationtitleJournal of Systems Architectureen
dc.identifier.publicationvolume128
dc.identifier.urihttps://hdl.handle.net/10016/35906
dc.identifier.uxxiAR/0000030705
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDComunidad de Madrid. S2018/TCS-4496es
dc.relation.projectIDGobierno de España. PID2019-104207RB-I00es
dc.relation.projectIDGobierno de España. TSI-063000-2021-127es
dc.relation.projectIDGobierno de España. RED2018-102585-Tes
dc.rights© 2022 Elsevier B.V. All rights reserved.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsembargoed accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaInformáticaes
dc.subject.otherCADen
dc.subject.otherHardware securityen
dc.subject.otherHardware trojansen
dc.subject.otherMachine learningen
dc.subject.otherMicroprocessorsen
dc.subject.otherRISC-Ven
dc.subject.otherSRAM-based FPGAen
dc.titleIs your FPGA bitstream Hardware Trojan-free? Machine learning can provide an answeren
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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