Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152417
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dc.contributor.authorGu, Chongyanen_US
dc.contributor.authorChang, Chip Hongen_US
dc.contributor.authorLiu, Weiqiangen_US
dc.contributor.authorYu, Shichaoen_US
dc.contributor.authorWang, Yaleen_US
dc.contributor.authorO’Neill, Máireen_US
dc.date.accessioned2021-08-11T06:05:59Z-
dc.date.available2021-08-11T06:05:59Z-
dc.date.issued2020-
dc.identifier.citationGu, C., Chang, C. H., Liu, W., Yu, S., Wang, Y. & O’Neill, M. (2020). A modeling attack resistant deception technique for securing lightweight-PUF based authentication. IEEE Transactions On Computer-Aided Design of Integrated Circuits and Systems, 40(6), 1183-1196. https://dx.doi.org/10.1109/TCAD.2020.3036807en_US
dc.identifier.issn0278-0070en_US
dc.identifier.urihttps://hdl.handle.net/10356/152417-
dc.description.abstractSilicon physical unclonable function (PUF) has emerged as a promising spoof-proof solution for low-cost device authentication. Due to practical constraints in preventing phishing through public network or insecure communication channels, simple PUF-based authentication protocol with unrestricted queries and transparent responses is vulnerable to modeling and replay attacks. Although PUF itself is lightweight, the ancillary cryptographic primitives required to support secure handshaking in classical PUF-based authentication protocol is not necessarily so. In this paper, we present a modeling attack resistant PUFbased mutual authentication scheme to mitigate the practical limitations in applications where a resource-rich server authenticates a device with no strong restriction imposed on the type of PUF design or any additional protection on the binary channel used for the authentication. Our scheme uses an active deception protocol to prevent machine learning (ML) attacks on a device with a monolithic integration of a genuine Strong PUF (SPUF), a fake PUF, a pseudo random number generator (PRNG), a register, a binary counter, a comparator and a simple controller. The hardware encapsulation makes collection of challenge response pairs (CRPs) easy for model building during enrollment but prohibitively time-consuming upon device deployment through the same interface. A genuine server can perform a mutual authentication with the device using a combined fresh challenge contributed by both the server and the device. The message exchanged in clear cannot be manipulated by the adversary to derive unused authentic CRPs. The adversary will have to either wait for an impractically long time to collect enough real CRPs by directly querying the device or the ML model derived from the collected CRPs will be poisoned to expose the imposer when it is used to perform a spoofing attack. The false PUF multiplexing is fortified against prediction of waiting time by doubling the time penalty for every unsuccessful guess. Our implementation results on field programmable gate array (FPGA) device and security analysis have corroborated the low hardware overheads and attack resistance of the proposed deception protocol.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.language.isoenen_US
dc.relationMOE2018-T1-001-131 (RG87/18)en_US
dc.relation.ispartofIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systemsen_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCAD.2020.3036807en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA modeling attack resistant deception technique for securing lightweight-PUF based authenticationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchCentre for Integrated Circuits and Systemsen_US
dc.identifier.doi10.1109/TCAD.2020.3036807-
dc.description.versionAccepted versionen_US
dc.identifier.issue6en_US
dc.identifier.volume40en_US
dc.identifier.spage1183en_US
dc.identifier.epage1196en_US
dc.subject.keywordsMachine Learning Attacksen_US
dc.subject.keywordsPhysical Unclonable Functionen_US
dc.subject.keywordsAuthentication Protocolen_US
dc.subject.keywordsDeception Protocolen_US
dc.subject.keywordsPoison Attacken_US
dc.description.acknowledgementThis work was supported in part by the Singapore MOE Tier 1 under Grant MOE2018-T1-001-131 RG87/18(S); in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N508664/-CSIT2; and in part by the National Natural Science Foundation of China under Grant 62022041 and Grant 61771239.en_US
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