A Robust Area-Efficient Physically Unclonable Function With High Machine Learning Attack Resilience in 28-nm CMOS

Lai, You‐Cheng; Yao, Chun-Yen; Yang, Shao-Hong; Ying-Wei, Wu; Liu, Tsung-Te

doi:10.1109/tcsi.2021.3098018

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Although the reliability of PUFs can deteriorate when experiencing a wide variation in temperature or supply voltage variations, new research results show promising results. For example, the 28 nm CMOS PUF proposed in [19] demonstrated robust performance for temperatures in the range of −40 to 100 • C and voltages of 0.5 to 1.4 V.…”

Section: A Overviewmentioning

confidence: 99%

Group Secret Key Generation Using Physical Layer Security for UAV Swarm Communications

Jangsher,

Al-Dweik,

Iraqi

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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In unmanned aerial vehicle (UAV) swarm networks, a group secret key (GSK) is required to enable secured UAV-UAV communications, multicast, and broadcast transmission. Moreover, it can be used for device authentication. Therefore, this paper proposes an efficient GSK protocol denoted as the sequential secret group key (SSGK) algorithm for distributed UAV swarmsecured communications. The proposed protocol utilizes network coding to generate cooperation information that is transmitted on the channel and is uncorrelated with the generated secret key. The proposed protocol depends on the pairwise key generation (PKG) process between pairs of UAVs. However, not all possible pairwise agreements are performed to minimize the time and signaling overhead, i.e., the pairwise key agreement is performed only between selected UAVs as a compromise between the required overhead transmission resources and the achieved redundancy level. The obtained results show that a redundancy level of 4 is sufficient S. Jangsher, A.

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Section: A Overviewmentioning

confidence: 99%

Group Secret Key Generation Using Physical Layer Security for UAV Swarm Communications

Jangsher,

Al-Dweik,

Iraqi

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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“…PUF architectures have been continuously enhanced to improve resilience against learning attacks [7], [9], [10], [16], [22]. Other works have tried to design PUFs with nonlinear challenge-response relationship by operating in subthreshold regime [6], [29], or using amplifier chains [28]. Such solutions were effective in improving resilience to classical machine learning approaches [15], but none was shown resistant to recent attacks using deep neural networks [5], [19].…”

Section: Related Workmentioning

confidence: 99%

Design Exploration and Security Assessment of PUF-on-PUF Implementations

Stangherlin¹,

Wu²,

Patel³

et al. 2022

Preprint

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We design, implement, and assess the security of several variations of the PUF-on-PUF (POP) architecture. We perform extensive experiments with deep neural networks (DNNs), showing results that endorse its resilience to learning attacks when using APUFs with 6, or more, stages in the first layer. Compositions using APUFs with 2, and 4 stages are shown vulnerable to DNN attacks. We reflect on such results, extending previous techniques of influential bits to assess stage bias in APUF instances. Our data shows that compositions not always preserve security properties of PUFs, the size of PUFs used plays a crucial role. We implemented a testchip in 65 nm CMOS to obtain accurate measurements of uniformity, uniqueness, and response stability for our POP implementations. Measurement results show that minimum bit error rate is obtained when using APUFs with 8 stages in the first layer, while fewer APUF stages lead to a large spread of bit error rate across different chips.

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