A Response-Feedback-Based Strong PUF with Improved Strict Avalanche Criterion and Reliability
Abstract
:1. Introduction
- It proposes a response feedback PUF based on LFSR and APUF, which greatly enhances the nonlinearity and randomness of the structure and enhances its ability to resist ML modeling and GSAC through response cyclic feedback. At the same time, the structure will not be subjected to reverse attacks. The response feedback does not affect the feedback bits of LFSR, thus ensuring that the feedback polynomial of LFSR is the primitive polynomial and the PUF CRPs space is not affected.
- It proposes Two-Level Reliability Improvement (TLRI), which is based on traditional voting methods to enhance reliability. By incorporating a reliable bit screening mechanism, this method can reduce the number of voting and single-response generation cycles by more than 65% compared to pure voting methods when the reliability is improved to 95%.
- Through simulation, we have demonstrated for the first time that this structure has good GSAC; even in a worst-case scenario, its output flip probability only deviates from the ideal value (0.5) by approximately 0.05. At the same time, ML modeling attacks such as LR, DNN, and SVM have a prediction accuracy of no more than 60%.
2. Related Works
3. Grounded Theory
4. Proposed PUF
4.1. Overview of Proposed PUF
4.2. Security Analysis
4.3. Two-Level Reliability Improvement
5. Numerical Experiments
5.1. Reliablity Improved with TLRI
5.2. GSAC
5.3. Resistance to ML Attacks
5.4. Uniformity and Uniqueness
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Delay | 0 | 2 | 4 | 6 | 8 | 10 | 12 |
Feedback rate (%) | 100 | 82.88 | 66.53 | 51.41 | 38.51 | 27.52 | 18.82 |
0.02 | 0.05 | 0.1 | 0.15 | |
Reliability (%) | 99.15 | 97.88 | 95.81 | 93.72 |
0.02 | 0.05 | 0.1 | 0.15 | |
Delay | ||||
0 | 35 | 215 | 751 | 1649 |
10 | 17 (49%) | 119 (55%) | 467 (62%) | 1151 (70%) |
12 | 9 (26%) | 61 (28%) | 251 (33%) | 499 (30%) |
n | Delay | Uniformity (Avg,Std) | Uniqueness (Avg,Std) |
---|---|---|---|
32 | 10 | 49.54 (3.67) | 50.17 (1.41) |
12 | 49.56 (3.68) | 50.24 (3.54) | |
64 | 10 | 50.05 (2.79) | 50.00 (0.31) |
12 | 50.04 (2.83) | 50.03 (0.28) | |
128 | 10 | 49.93 (1.78) | 49.99 (0.21) |
12 | 49.87 (1.78) | 50.04 (0.17) |
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Zhu, B.; Jiang, X.; Huang, K.; Yu, M. A Response-Feedback-Based Strong PUF with Improved Strict Avalanche Criterion and Reliability. Sensors 2024, 24, 93. https://doi.org/10.3390/s24010093
Zhu B, Jiang X, Huang K, Yu M. A Response-Feedback-Based Strong PUF with Improved Strict Avalanche Criterion and Reliability. Sensors. 2024; 24(1):93. https://doi.org/10.3390/s24010093
Chicago/Turabian StyleZhu, Baokui, Xiaowen Jiang, Kai Huang, and Miao Yu. 2024. "A Response-Feedback-Based Strong PUF with Improved Strict Avalanche Criterion and Reliability" Sensors 24, no. 1: 93. https://doi.org/10.3390/s24010093
APA StyleZhu, B., Jiang, X., Huang, K., & Yu, M. (2024). A Response-Feedback-Based Strong PUF with Improved Strict Avalanche Criterion and Reliability. Sensors, 24(1), 93. https://doi.org/10.3390/s24010093