Design and Development of an ML/DL Attack Resistance of RC-Based PUF for IoT Security
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), ...
| Authors | Joy Acharya, Smit Patel, Paawan Sharma et al. |
|---|---|
| Primary category | cs.CR |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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