Late Breaking Results: Automatic Anomaly Detection Method in Physical Unclonable Functions using Data Mining Techniques
Mohammad Reza Heidari Iman, Sergio Vinagrero Gutiérrez, Elena Ioana Vatajelu, Giorgio Di Natale · 2025
Physical Unclonable Functions (PUFs) present a promising alternative to traditional cryptographic techniques for securing sensitive information in modern circuits. By exploiting inherent process variability, PUFs generate unique secrets dynamically, thus eliminating the need for data storage. However, a major challenge in PUF-based security is distinguishing valid PUFs from those that may have been tampered with or are invalid (i.e., not belonging to the original design). This paper proposes a data mining-based approach for detecting anomalies and identifying tampered or invalid PUFs. The proposed method mines a set of rules that describe the expected behavior of the PUF, with deviations from these rules signaling potential security issues and vulnerabilities. Experimental results demonstrate that the method effectively identifies invalid or tampered PUFs, showcasing its potential for enhancing PUF-based security systems.