Tools
Tools
Tu, Z, Xue, Y, Ren, P, Hao, F, Wang, R, Li, M, Zhang, JF, Ji, Z and Huang, R (2021) A Probability-based Strong Physical Unclonable Function with Strong Machine Learning Immunity. IEEE Electron Device Letters. ISSN 0741-3106
|
Text
EDL-2021 Ji PUF.pdf - Accepted Version Download (809kB) | Preview |
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: | Article |
|---|---|
| Additional Information: | © 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. |
| Uncontrolled Keywords: | 0906 Electrical and Electronic Engineering |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Civil Engineering & Built Environment |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Date Deposited: | 13 Dec 2021 14:22 |
| Last Modified: | 13 Dec 2021 14:30 |
| DOI or ID number: | 10.1109/led.2021.3130606 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/15915 |
 |
View Item |