A Modeling Attack Resistant Deception Technique for Securing Lightweight-PUF-Based Authentication

Gu, Chongyan; Chang, Chip-Hong; Liu, Weiqiang; Yu, Shengsheng; Wang, Yale; O’Neill, Máire

doi:10.1109/tcad.2020.3036807

Cited by 45 publications

(14 citation statements)

References 43 publications

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“…To address this limitation, Gu et al [12] proposed a device-to-device PUF based protocol which uses deception techniques, e.g., a fake PUF circuit and random numbers, to prevent the adversary from predicting the PUF responses.…”

Section: Access Decision Figure 8 Location-based Authenticationmentioning

confidence: 99%

A Survey on Smart Home Authentication: Toward Secure, Multi-Level and Interaction-Based Identification

2021

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With the increased number and reduced cost of smart devices, Internet of Things (IoT) applications such as smart home (SHome) are increasingly popular. Owing to the characteristics of IoT environments such as resource constrained devices, existing authentication solutions may not be suitable to secure these environments. As a result, a number of authentication solutions specifically designed for IoT environments have been proposed. This paper provides a critical analysis of existing authentication solutions. The major contributions of the paper are as follows. First, it presents a generic model derived from an SHome use-case scenario. Secondly, based on the model, it performs a threat analysis to identify possible means of attacks. The analysis leads to the specification of a set of desirable security requirements for the design of authentication solutions for SHome. Thirdly, based on the requirements, existing authentication solutions are analysed and some ideas for achieving effective and efficient authentication in IoT environments are proposed.

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Section: Access Decision Figure 8 Location-based Authenticationmentioning

confidence: 99%

A Survey on Smart Home Authentication: Toward Secure, Multi-Level and Interaction-Based Identification

2021

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“…The proposed TH-PUF mitigates the attack of LR and CMA-ES attacks compared with traditional PUF designs, because the adversary needs more time and resources to collect enough CRPs to achieve higher prediction rates for the proposed PUF. To completely thwart machine learning attacks, there are many other protocol-based approaches, such as deception protocol [36], that can be applied to the proposed TH-PUF. The prediction rates of different stages of the proposed TH-PUF to LR and CMA-ES are reported in TABLE IV; an increased complexity of the proposed TH-PUF is beneficial to security, as the 64-bit TH-PUF shows robustness to the attacks.…”

Section: Security Analysismentioning

confidence: 99%

A Physical Unclonable Function Using a Configurable Tristate Hybrid Scheme With Non-Volatile Memory

Cui

et al. 2021

IEEE Open J. Nanotechnol.

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The physical unclonable function (PUF) is a promising low-cost hardware security primitive. Recent advances in nanotechnology have provided new opportunities for nanoscale PUF circuits. The resistive random access memory (RRAM) is extensively used in nanoscale circuits due to its low cost, non-volatility and easy integration with CMOS. This paper proposes a novel tristate hybrid PUF (TH-PUF) design based on a one-transistor-one-RRAM (1T1R) cell; this cell can be configured into two weak PUFs and a strong PUF using few control signals. To assess the proposed PUF design, a compact RRAM model at UMC 65nm technology is employed. Simulation results show that the proposed TH-PUF achieves good uniqueness, reliability as well as a higher gate usability compared with an entire CMOS PUFs. The number of challenge response pairs (CRPs) of the proposed TH-PUF is larger than other RRAM-based PUFs. Moreover, the TH-PUF is more resistant to a modeling machine learning attack than traditional PUF designs.

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“…Poisoning the PUF's response to mislead the adversary is another approach to prevent ML attacks. One such method is proposed in [116], where a duplicated or a fake PUF, is embedded in the chip alongside the original PUF to delay the adversary from collecting sufficient correct CRPs for training. It provides fake CRPs for consecutive authentication requests made within a waiting time to mislead the adversary in building an incorrect PUF model.…”

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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A Modeling Attack Resistant Deception Technique for Securing Lightweight-PUF-Based Authentication

Cited by 45 publications

References 43 publications

A Survey on Smart Home Authentication: Toward Secure, Multi-Level and Interaction-Based Identification

A Survey on Smart Home Authentication: Toward Secure, Multi-Level and Interaction-Based Identification

A Physical Unclonable Function Using a Configurable Tristate Hybrid Scheme With Non-Volatile Memory

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

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