DA PUF: dynamic adversarial PUF against machine learning attacks

Yuping Kou; Haolin Jiang; Ding Deng; Weihua Mou

doi:10.1117/12.3029222

22 May 2024 DA PUF: dynamic adversarial PUF against machine learning attacks

Yuping Kou, Haolin Jiang, Ding Deng, Weihua Mou

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317610 (2024) https://doi.org/10.1117/12.3029222
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

With the rise of the Internet of Things (IoT), some emerging mobile devices have been widely used such as wireless sensor networks, Radio Frequency Identification (RFID) chips, and smart cards etc. However, their communication security issues in open environments are increasingly prominent. Physical Unclonable Function (PUF) is a new type of "hardware fingerprint" that can authenticate IoT devices in the aspect of hardware. However, the Challenge-Response Pair (CRP) mechanism of PUF is vulnerable to Machine Learning (ML) modeling attacks. Based on this, the paper proposes a Dynamic Adversarial (DA) PUF through modifying the original CRP mechanism of APUF. Experimental results show that the PUF can effectively resist ML modeling attacks, while maintaining good uniformity, uniqueness, and reliability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuping Kou, Haolin Jiang, Ding Deng, and Weihua Mou "DA PUF: dynamic adversarial PUF against machine learning attacks", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317610 (22 May 2024); https://doi.org/10.1117/12.3029222

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