Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143895
Title: Machine-learning attacks on PolyPUFs, OB-PUFs, RPUFs, LHS-PUFs, and PUF–FSMs
Authors: Delvaux, Jeroen
Keywords: Science::Physics
Issue Date: 2019
Source: Delvaux, J. (2019). Machine-Learning Attacks on PolyPUFs, OB-PUFs, RPUFs, LHS-PUFs, and PUF–FSMs. IEEE Transactions on Information Forensics and Security, 14(8), 2043–2058. doi:10.1109/tifs.2019.2891223
Journal: IEEE Transactions on Information Forensics and Security
Abstract: A physically unclonable function (PUF) is a circuit of which the input-output behavior is designed to be sensitive to the random variations of its manufacturing process. This building block hence facilitates the authentication of any given device in a population of identically laid-out silicon chips, similar to the biometric authentication of a human. The focus and novelty of this paper is the development of efficient impersonation attacks on the following five Arbiter PUF-based authentication protocols: 1) the so-called Poly PUF protocol of Konigsmark et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2016; 2) the so-called OB-PUF protocol of Gao et al. as presented at the IEEE Conference PerCom 2016; 3) the so-called RPUF protocol of Ye et al. as presented at the IEEE Conference AsianHOST 2016; 4) the so-called LHS-PUF protocol of Idriss and Bayoumi as presented at the IEEE Conference RFID-TA 2017; and 5) the so-called PUF-FSM protocol of Gao et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2018. The common flaw of all five designs is that the use of lightweight obfuscation logic provides insufficient protection against machine-learning attacks.
URI: https://hdl.handle.net/10356/143895
ISSN: 1556-6013
DOI: 10.1109/TIFS.2019.2891223
Rights: © 2019 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/TIFS.2019.2891223.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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