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

2026-03-26

AI Summary

This work addresses the vulnerability of physical unclonable functions (PUFs) in Internet of Things (IoT) devices to machine learning (ML)-based modeling attacks by proposing a lightweight, dynamically reconfigurable RC-PUF architecture with minimal structural complexity and resource overhead. The design enhances nonlinearity and randomness through 32-bit challenge-response pairs and rigorously evaluates resistance to ML/DL attacks using multiple advanced models—including artificial neural networks (ANN), gradient-boosted neural networks (GBNN), decision trees (DT), random forests (RF), and XGBoost—trained on systematically partitioned datasets. Experimental results demonstrate that all attack models achieve prediction accuracies on the test set close to random guessing (50%–53%), thereby significantly improving robustness against ML/DL-based modeling attacks and confirming the effectiveness and practicality of the proposed scheme for hardware security applications.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Adversarial Attacks & Robustness
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• PUF
• ML/DL attacks
• IoT security
• challenge-response pairs
• modeling attacks

Innovation

• RC-PUF
• machine learning resistance
• dynamically reconfigurable
• IoT security
• challenge-response pairs