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A probability-based strong physical unclonable function with strong machine learning immunity
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Tu, Zezhong, Xue, Yongkang, Ren, Pengpeng, Hao, Feng, Wang, Runsheng, Li, Meng, Zhang, Jianfu, Ji, Zhigang and Huang, Ru (2022) A probability-based strong physical unclonable function with strong machine learning immunity. IEEE Electron Device Letters, 43 (1). pp. 138-141. doi:10.1109/LED.2021.3130606
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WRAP-Probability-based-strong-physical-unclonable-strong-machine-2021.pdf - Accepted Version - Requires a PDF viewer. Download (961Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/LED.2021.3130606
Abstract
A novel strong physical unclonable function (PUF), called Probability-based PUF (Prob-PUF), is proposed using the stochastic process of trap emission in nano-scaled transistors. For the first time, the information of trap emission probability is used in the PUF design. This new approach offers ideal immunity to machine learning (ML) attacks. Since Prob-PUF only stores a mathematical model, it naturally avoids the dilemma between the requirement of a large number of challenge-response pairs (CRPs) and the limited storage space, making it a potential solution for future secure storage.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Machine learning, Computer security, Adaptive computing systems, Data encryption (Computer science) -- Mathematics | ||||||
| Journal or Publication Title: | IEEE Electron Device Letters | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 0741-3106 | ||||||
| Official Date: | January 2022 | ||||||
| Dates: |
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| Volume: | 43 | ||||||
| Number: | 1 | ||||||
| Page Range: | pp. 138-141 | ||||||
| DOI: | 10.1109/LED.2021.3130606 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | © 2021 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. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| RIOXX Funder/Project Grant: |
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