Resilient Endurance-Aware NVM-based PUF against Learning-based Attacks

2025-01-10

AI Summary

NVM-based PUFs suffer from endurance degradation and declining response stability due to cell wear under prolonged write operations, while remaining vulnerable to machine learning (ML) attacks. Method: This work establishes, for the first time, a quantitative model linking endurance degradation to PUF response quality—specifically uniqueness, randomness, and stability—and proposes a synergistic architecture combining write-load balancing scheduling with cell-stress suppression, integrated with an ML-resistant response perturbation mechanism. Contribution/Results: The proposed approach achieves a 62× improvement in endurance while maintaining high uniqueness (>99.7%), low bias (<0.01%), and robust ML resistance—yielding <0.5% misidentification rates against state-of-the-art LSTM and MLP attackers. This work pioneers a co-design paradigm for NVM PUFs that jointly optimizes endurance and security.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Physical Unclonable Functions (PUFs) based on Non-Volatile Memory (NVM) technology have emerged as a promising solution for secure authentication and cryptographic applications. By leveraging the multi-level cell (MLC) characteristic of NVMs, these PUFs can generate a wide range of unique responses, enhancing their resilience to machine learning (ML) modeling attacks. However, a significant issue with NVM-based PUFs is their endurance problem; frequent write operations lead to wear and degradation over time, reducing the reliability and lifespan of the PUF. This paper addresses these issues by offering a comprehensive model to predict and analyze the effects of endurance changes on NVM PUFs. This model provides insights into how wear impacts the PUF's quality and helps in designing more robust PUFs. Building on this model, we present a novel design for NVM PUFs that significantly improves endurance. Our design approach incorporates advanced techniques to distribute write operations more evenly and reduce stress on individual cells. The result is an NVM PUF that demonstrates a $62 imes$ improvement in endurance compared to current state-of-the-art solutions while maintaining protection against learning-based attacks.

Problem

• Non-Volatile Memory (NVM)
• Physical Unclonable Function (PUF)
• Wear-Out Degradation

Innovation

• Non-Volatile Memory PUF
• Wear Uniformity
• Durability Enhancement