Ali Ahmadi Shahrakht1, Parisa Hajirahimi2, Omid Rostami3, Diego Martín4,*
Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3059-3081, 2023, DOI:10.32604/iasc.2023.040502
Abstract As the internet of things (IoT) continues to expand rapidly, the significance of its security concerns has grown in recent years. To address these concerns, physical unclonable functions (PUFs) have emerged as valuable tools for enhancing IoT security. PUFs leverage the inherent randomness found in the embedded hardware of IoT devices. However, it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches. In this paper, a new deep learning (DL)-based modeling attack is introduced to break the resistance of complex XAPUFs. Because training DL models is a problem that falls under the category of NP-hard… More >
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