Design of Strong Physical Unclonable Function Circuit Against Machine Learning Attacks Based on Chaos Mapping

WANG Pengjun; FANG Haoran; LI Gang

doi:10.11999/JEIT231129

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WANG Pengjun, FANG Haoran, LI Gang. Design of Strong Physical Unclonable Function Circuit Against Machine Learning Attacks Based on Chaos Mapping[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231129

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WANG Pengjun, FANG Haoran, LI Gang. Design of Strong Physical Unclonable Function Circuit Against Machine Learning Attacks Based on Chaos Mapping[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231129

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Design of Strong Physical Unclonable Function Circuit Against Machine Learning Attacks Based on Chaos Mapping

doi: 10.11999/JEIT231129

College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China

Funds: The National Natural Science Foundation of China (62234008, 62374117), The Wenzhou Basic Scientific Research Projects (G20220005)

Received Date: 2023-10-17
Rev Recd Date: 2024-01-21

Available Online: 2024-01-29

Abstract

Abstract

Physical Unclonable Function (PUF) has broad application prospects in the field of hardware security, but it is susceptible to modeling attacks based on machine learning. By studying the strong PUF circuit structure and chaotic mapping mechanism, a PUF circuit that can effectively resist machine learning modeling attacks is proposed. This circuit takes the original excitation as the initial value of the chaotic mapping, utilizes the internal relationship between the PUF excitation response mapping time and the iteration depth of the chaotic algorithm to generate unpredictable chaotic values, and uses PUF intermediate response feedback to encrypt the excitation. It can further improve the complexity of excitation and response mapping, thereby enhancing the resistance of PUF to machine learning attacks. The PUF is implemented using Artix-7 FPGA. The test results show that even with up to 1 million sets of excitation response pairs selected, the attack prediction rate based on logistic regression, support vector machine, and artificial neural network is still close to the ideal value of 50%. And the PUF has good randomness, uniqueness, and stability.
- Physical Unclonable Function(PUF),
- Machine learning,
- Chaotic mapping,
- Response feedback

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References(20)

References

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