An Innovative Data Mining Technique for Automatic Anomaly Detection in Physical Unclonable Functions

Physical Unclonable Functions (PUFs) offer a promising alternative to conventional cryptographic techniques to secure sensitive information in modern circuits. PUFs leverage inherent process variability to dynamically generate unique secrets, eliminating the need for data storage. However, a significant challenge in PUF-based security is differentiating between valid PUFs and those that may have been tampered with or are invalid (i.e., not belonging to the original design). This paper presents an innovative data mining-based technique for detecting anomalies and identifying tampered or invalid PUFs. The proposed method extracts a set of rules that describe the expected behavior of the PUF, where deviations from these rules indicate potential security issues and vulnerabilities. Experimental results demonstrate that the method effectively detects invalid or tampered PUFs, highlighting its potential to strengthen PUF-based security systems.