Controlled Physical Unclonable Function Research Based on Sensitivity Confusion Mechanism

Jinfu XU; Jin WU; Junwei LI; Tongzhou QU; Yongxing DONG

doi:10.11999/JEIT180775

Volume 41 Issue 7

Jul. 2019

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Jinfu XU, Jin WU, Junwei LI, Tongzhou QU, Yongxing DONG. Controlled Physical Unclonable Function Research Based on Sensitivity Confusion Mechanism[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1601-1609. doi: 10.11999/JEIT180775

Citation:

Jinfu XU, Jin WU, Junwei LI, Tongzhou QU, Yongxing DONG. Controlled Physical Unclonable Function Research Based on Sensitivity Confusion Mechanism[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1601-1609. doi: 10.11999/JEIT180775

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Controlled Physical Unclonable Function Research Based on Sensitivity Confusion Mechanism

doi: 10.11999/JEIT180775

The Information Engineering University, Zhengzhou 450001, China

Received Date: 2018-08-06
Rev Recd Date: 2019-02-11

Available Online: 2019-03-23

Publish Date: 2019-07-01

Abstract

Abstract

In order to overcome the vulnerability of Physical Unclonable Function (PUF) to modeling attacks, a controlled PUF architecture based on sensitivity confusion mechanism is proposed. According to the Boolean function definition of PUF and Walsh spectrum theory, it is derived that each excitation bit has different sensitivity, and the position selection rules related to the parity of the confound value bit width are analyzed and summarized. This rule guides the design of the Multi-bit Wide Confusion Algorithm (MWCA) and constructs a controlled PUF architecture with high security. The basic PUF structure is evaluated as a protective object of the controlled PUF. It is found that the response generated by the controlled PUF based on the sensitivity confusion mechanism has better randomness. Logistic regression algorithm is used to model different PUF attack. The experimental results show that compared with the basic ROPUF, the arbiter PUF and the OB-PUF based on the random confusion mechanism, the controlled PUF based on the sensitivity confusion mechanism can significantly improve the PUF resistance capabilities for modeling attack.
- Information security,
- Machine learning,
- Boolean function,
- Sensitivity information

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References(15)

References

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