Attacking Delay-Based PUFs With Minimal Adversarial Knowledge

• Published in: IEEE transactions on information forensics and security Vol. 19; pp. 7259 - 7274
• Main Authors: Hongming, Fei, Millwood, Owen, Gope, Prosanta, Miskelly, Jack, Sikdar, Biplab
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: Authentication; Cryptography; Integrated circuit modeling; Load modeling; machine learning-modelling attacks (ML-MA); Mathematical models; minimal adversarial knowledge; Physical unclonable function (PUF); Security; Training
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2024.3433548

Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelling-resistant PUFs and devising machine learning attacks against them. This dichotomy often results in exaggerated concerns and overconfidence in PUF security, primarily because there lacks a universal tool to gauge a PUF's security. In many scenarios, attacks require additional information, such as PUF type or configuration parameters. Alarmingly, new PUFs are often branded 'secure' if they lack a specific attack model upon introduction. To impartially assess the security of delay-based PUFs, we present a generic framework featuring a Mixture-of-PUF-Experts (MoPE) structure for mounting attacks on various PUFs with minimal adversarial knowledge, which provides a way to compare their performance fairly and impartially. We demonstrate the capability of our model to attack different PUF types, including the first successful attack on Heterogeneous Feed-Forward PUFs using only a reasonable amount of challenges and responses. We propose an extension version of our model, a Multi-gate Mixture-of-PUF-Experts (MMoPE) structure, facilitating multi-task learning across diverse PUFs to recognise commonalities across PUF designs. This allows a streamlining of training periods for attacking multiple PUFs simultaneously. We conclude by showcasing the potent performance of MoPE and MMoPE across a spectrum of PUF types, employing simulated, real-world unbiased, and biased data sets for analysis.