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

Joy Acharya, Smit Patel, Paawan Sharma, Mohendra Roy

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.

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

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.

Paper Structure

This paper contains 9 sections, 2 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Dataset Generation
  4. Machine Learning Models
  5. Deep Learning Models
  6. Error Functions
  7. Evaluation Metrics
  8. Results and Discussion
  9. Conclusion

Figures (4)

  • Figure 1: RC-PUF dataset generation workflow showing 32-bit challenge input, analog delay measurement, and digital response storage.
  • Figure 2: Normalised step vs. training accuracy for all ML/DL models, showing convergence toward near-perfect training accuracy across ANN, GBNN, XGBoost, Decision Tree, and Random Forest.
  • Figure 3: Normalised step vs. validation accuracy for all ML/DL models, showing fluctuations close to the random-guess baseline across ANN, GBNN, XGBoost, Decision Tree, and Random Forest.
  • Figure 4: Comparison of test accuracy across different ML/DL models for RC-PUF modeling attacks.