[2603.28798] Design and Development of an ML/DL Attack Resistance of RC-Based PUF for IoT Security

Computer Science > Cryptography and Security arXiv:2603.28798 (cs) [Submitted on 26 Mar 2026] Title:Design and Development of an ML/DL Attack Resistance of RC-Based PUF for IoT Security Authors:Joy Acharya, Smit Patel, Paawan Sharma, Mohendra Roy View a PDF of the paper titled Design and Development of an ML/DL Attack Resistance of RC-Based PUF for IoT Security, by Joy Acharya and 3 other authors View PDF HTML (experimental) Abstract:Physically Unclonable Functions (PUFs) provide promising hardware security for IoT authentication, leveraging inherent randomness suitable for resource constrained environments. However, ML/DL modeling attacks threaten PUF security by learning challenge-response patterns. This work introduces a custom resistor-capacitor (RC) based dynamically reconfigurable PUF using 32-bit challenge-response pairs (CRPs) designed to resist such attacks. We systematically evaluated robustness by generating a CRP dataset and splitting it into training, validation, and test sets. Multiple ML techniques including Artificial Neural Networks (ANN), Gradient Boosted Neural Networks (GBNN), Decision Trees (DT), Random Forests (RF), and XGBoost, were trained to model PUF behavior. While all models achieved 100% training accuracy, test performance remained near random guessing: 51.05% (ANN), 53.27% (GBNN), 50.06% (DT), 52.08% (RF), and 50.97% (XGBoost). These results demonstrate the proposed PUF's strong resistance to ML-driven modeling attacks, as advanced algorithms ...