On the Encryption of the Challenge in Physically Unclonable Functions

Vatajelu, Elena Ioana; Natale, Giorgio Di; Mispan, Mohd Syafiq; Halak, Basel

doi:10.1109/iolts.2019.8854387

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…A PUF response could be affected by noise caused by power variation, and environmental conditions, e.g., temperature [57]. Such a noise is often mitigated by the incorporation of an error correction code (ECC) at the circuit-level.…”

Section: A System and Threat Modelsmentioning

confidence: 99%

A PUF-Based Modeling-Attack Resilient Authentication Protocol for IoT Devices

Ebrahimabadi

Younis

Karimi

2022

IEEE Internet Things J.

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Physical Unclonable Functions (PUFs) offer a promising solution for authentication of IoT devices as they provide unique fingerprints for the underlying devices through their challenge-response pairs. However, PUFs have been shown to be vulnerable to modeling attacks. In this paper, we propose a novel protocol to thwart such vulnerability by limiting the adversary's ability to intercept the whole challenge bits exchanged with IoT nodes. We split the challenge bits over multiple messages and engage one or multiple helper nodes in the dissemination process. We further study the implications of various parts of the challenge patterns on the modeling attack and propose extensions of our protocol that exploit bits scrambling and padding to ameliorate the attack resiliency. The experimental results extracted from a 16-bit and a 64-bit arbiter-PUF implemented on FPGA demonstrate the effectiveness of the proposed methods in boosting the robustness of IoT authentication.

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Section: A System and Threat Modelsmentioning

confidence: 99%

A PUF-Based Modeling-Attack Resilient Authentication Protocol for IoT Devices

Ebrahimabadi

Younis

Karimi

2022

IEEE Internet Things J.

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“…Despite being labelled as "weak", these PUFs have seen much more usage in practice as they generally have quite a high cost for an adversary to model [19,53]. Typically, weak PUFs are jointly considered with cryptographic methods [65] (e.g., encryption) to compensate for CRP scarcity.…”

Section: Puf Designs -Backgroundmentioning

confidence: 99%

DTA-PUF: Dynamic Timing-aware Physical Unclonable Function for Resource-constrained Devices

Tsiokanos

Miskelly

et al. 2021

J. Emerg. Technol. Comput. Syst.

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In recent years, physical unclonable functions (PUFs) have gained a lot of attention as mechanisms for hardware-rooted device authentication. While the majority of the previously proposed PUFs derive entropy using dedicated circuitry, software PUFs achieve this from existing circuitry in a system. Such software-derived designs are highly desirable for low-power embedded systems as they require no hardware overhead. However, these software PUFs induce considerable processing overheads that hinder their adoption in resource-constrained devices. In this article, we propose DTA-PUF, a novel, software PUF design that exploits the instruction- and data-dependent dynamic timing behaviour of pipelined cores to provide a reliable challenge-response mechanism without requiring any extra hardware. DTA-PUF accepts sequences of instructions as an input challenge and produces an output response based on the manifested timing errors under specific over-clocked settings. To lower the required processing effort, we systematically select instruction sequences that maximise error-rate. The application to a post-layout pipelined floating-point unit, which is implemented in 45 nm process technology, demonstrates the effectiveness and practicability of our PUF design. Finally, DTA-PUF requires up to 50× fewer instructions than existing software processor PUF designs, limiting processing costs and resulting in up to 26% power savings.

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“…These schemes enhance the security against ML-based modeling attacks at the expense of imposing high area overhead. For example, in the scheme proposed in [109], the target strong PUF receives an encrypted instead of a plain challenge. The encryption is performed by an embedded symmetric cipher whose key bits are generated via a weak PUF resided in the same chip.…”

Section: B Modeling Attacks On Pufs and Robustness Enhancementsmentioning

confidence: 99%

Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security

Liu

Chang

Wang

et al. 2021

IEEE J. Emerg. Sel. Topics Circuits Syst.

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The last decade has witnessed remarkable research advances at the intersection of machine learning (ML) and hardware security. The confluence of the two technologies has created many interesting and unique opportunities, but also left some issues in their wake. ML schemes have been extensively used to enhance the security and trust of embedded systems like hardware Trojans and malware detection. On the other hand, ML-based approaches have also been adopted by adversaries to assist side-channel attacks, reverse engineer integrated circuits and break hardware security primitives like Physically Unclonable Functions (PUFs). Deep learning is a subfield of ML. It can continuously learn from a large amount of labeled data with a layered structure. Despite the impressive outcomes demonstrated by deep learning in many application scenarios, the dark side of it has not been fully exposed yet. The inability to fully understand and explain what has been done within the super-intelligence can turn an inherently benevolent system into malevolent. Recent research has revealed that the outputs of Deep Neural Networks (DNNs) can be easily corrupted by imperceptibly small input perturbations. As computations are brought nearer to the source of data creation, the attack surface of DNN has also been extended from the input data to the edge devices. Accordingly, due to the opportunities of MLassisted security and the vulnerabilities of ML implementation, in this paper, we will survey the applications, vulnerabilities and fortification of ML from the perspective of hardware security. We will discuss the possible future research directions, and thereby, sharing a roadmap for the hardware security community in general.

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On the Encryption of the Challenge in Physically Unclonable Functions

Cited by 15 publications

References 19 publications

A PUF-Based Modeling-Attack Resilient Authentication Protocol for IoT Devices

A PUF-Based Modeling-Attack Resilient Authentication Protocol for IoT Devices

DTA-PUF: Dynamic Timing-aware Physical Unclonable Function for Resource-constrained Devices

Two Sides of the Same Coin: Boons and Banes of Machine Learning in Hardware Security

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