Design and Development of an ML/DL Attack Resistance of RC-Based PUF for IoT Security
arXiv:2603.28798v1 Announce Type: new 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
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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 fail to reproduce accurate responses. The dynamically reconfigurable architecture enhances robustness against adversarial threats with minimal resource overhead. This simple RC-PUF offers an effective, low-cost alternative to complex encryption for securing next-generation IoT authentication against machine learning-based threats, ensuring reliable device verification without compromising computational efficiency or scalability in deployed IoT networks.
Comments: This paper has been accepted for the IEEE GCON 2026 conference, organized by IIT Guwahati
Subjects:
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2603.28798 [cs.CR]
(or arXiv:2603.28798v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2603.28798
arXiv-issued DOI via DataCite
Submission history
From: Mohendra Roy (PhD) [view email] [v1] Thu, 26 Mar 2026 11:49:53 UTC (332 KB)
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