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Title: Coin Flipping PUF: A Novel PUF with Improved Resistance against Machine Learning Attacks
Authors: Tanaka, Yuki
Bian, Song  kyouindb  KAKEN_id
Hiromoto, Masayuki  kyouindb  KAKEN_id
Sato, Takashi  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 田中, 悠貴
辺, 松
廣本, 正之
佐藤, 高史
Keywords: PUF
Hardware Security
Machine Learning
Ring Oscillator
Bistable Ring
Issue Date: May-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Journal title: IEEE Transactions on Circuits and Systems II: Express Briefs
Volume: 65
Issue: 5
Start page: 602
End page: 606
Abstract: We propose a novel coin-flipping physically unclonable function (CF-PUF) that significantly improves the resistance against machine-learning attacks. The proposed PUF utilizes the strong nonlinearity of the convergence time of bistable rings (BRs) with respect to variations in the threshold voltage. The response is generated based on the instantaneous value of a ring oscillator at the convergence time of the corresponding BR, which is running in parallel. SPICE simulations show that the prediction accuracy of support-vector machine (SVM) on the responses of CF-PUF is around 50 percent, which means that SVM cannot predict better than random guesses.
Rights: © 2018 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.
This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1109/TCSII.2018.2821267
Appears in Collections:Journal Articles

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