DA PUF: dynamic adversarial PUF against machine learning attacks
Yuping Kou, Haolin Jiang, Ding Deng, Weihua Mou · 2024
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.