Attacking Delay-based PUFs with Minimal Adversary Model: A Comprehensive Analysis

מושגי ליבה

The author presents a generic framework for attacking delay-based PUFs with minimal adversarial knowledge, showcasing successful attacks on various PUF types. The approach allows for fair and impartial comparison of performance across different PUF designs.

תקציר

Physically Unclonable Functions (PUFs) are vulnerable to modelling attacks that exploit correlations between inputs and outputs. The proposed Mixture-of-PUF-Experts (MoPE) structure enables successful attacks on diverse PUF types with minimal adversarial knowledge. The study highlights the importance of a universal tool to evaluate PUF security.

The research addresses lightweight device authentication challenges in the IoT era, emphasizing the efficiency and robustness of delay-based Arbiter PUFs against malicious attacks. By presenting a generic framework for attacking multiple PUFs simultaneously, the study contributes to enhancing security measures in device authentication systems.

The analysis delves into machine learning modelling attacks on Strong PUFs, showcasing successful predictions of challenge-response pairs using various ML algorithms. The proposed Multi-gate Mixture-of-Experts Model aims to improve model quality and efficiency by exploiting commonalities across different tasks.

סטטיסטיקה

R¨uhrmair et al.'s method achieved >97.0% accuracy with 8k CRPs.
Aseeri et al.'s method reached 97.0% accuracy with 8k CRPs.
Mursi et al.'s approach demonstrated >98.0% accuracy with 8k CRPs.
Proposed Scheme achieved >94.0% accuracy with 8k CRPs.
R¨uhrmair et al.'s method showed >99.0% accuracy with 20k CRPs.
Aseeri et al.'s method attained >97.0% accuracy with 24k CRPs.
Mursi et al.'s approach showcased >98.0% accuracy with 24k CRPs.
Proposed Scheme achieved >95% accuracy with 24k CRPs.

ציטוטים

"Physically Unclonable Functions provide a streamlined solution for lightweight device authentication." - Content
"In many scenarios, attacks require additional information such as PUF type or configuration parameters." - Content

תובנות מפתח מזוקקות מ:

Attacking Delay-based PUFs with Minimal Adversary Model

by Hongming Fei... ב- arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00464.pdf

שאלות מעמיקות

Can machine learning modelling attacks be effectively countered without detailed knowledge of specific PUF structures

Machine learning modelling attacks can be effectively countered without detailed knowledge of specific PUF structures by using a generic framework like the one proposed in the context. This framework, based on a Mixture-of-PUF-Experts (MoPE) structure, allows for attacking various PUFs with minimal adversarial knowledge. By utilizing multiple experts and gate functions, the model can adapt to different PUF types without requiring prior information about their specific architectures. The key is to focus on the commonalities across PUF designs and leverage those similarities to create a robust attack tool that does not rely on intricate details of individual PUF structures.

How can the proposed generic framework adapt to evolving PUF designs and security threats

The proposed generic framework can adapt to evolving PUF designs and security threats by its inherent flexibility and scalability. With the ability to handle multiple tasks simultaneously through multi-task learning, the model can easily accommodate new variations or configurations of delay-based PUFs without significant modifications. Additionally, features like Sparse Softmax activation help optimize training efficiency and prevent overfitting when dealing with complex or simple PUF structures. As new security threats emerge in IoT devices, this adaptable framework provides a versatile tool for assessing and countering potential vulnerabilities in lightweight device authentication systems.

What implications does the research have for enhancing overall IoT device security beyond just lightweight authentication

The research has significant implications for enhancing overall IoT device security beyond just lightweight authentication by providing a standardized approach to evaluating the security of Physically Unclonable Functions (PUFs). By developing a universal tool that can gauge a PUF's security level impartially and fairly across different types of delay-based PUFs, it enables researchers and developers to assess vulnerabilities more effectively. This comprehensive analysis helps identify weaknesses in existing systems, improve countermeasures against machine learning modelling attacks, and ultimately enhance the overall cybersecurity posture of IoT devices integrating lightweight authentication mechanisms.