ePUF: A lightweight double identity verification in IoT

Zhao, Bo; Zhao, Peng; Fan, Peiru

doi:10.26599/tst.2019.9010072

Cited by 26 publications

(17 citation statements)

References 31 publications

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“…Instead, visual attention networks [37][38][39][40][41] are proposed to automatically locate the informative regions without part and bounding box annotations by the self-attention mechanism, and show superiority in the fine-grained image classification task [21,[42][43][44]. Liu et al [21] use a reinforcement learning framework to adaptively locate the discriminative regions.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

Tian

Zhang

et al. 2021

Complexity

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Autonomous object detection powered by cutting-edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine-grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine-grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories-subcategories and subcategories-attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high-level image features. Extensive experiments on the Caltech-UCSD Birds-200-2011 dataset verify the superiority of our proposed framework over several existing state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

Tian

Zhang

et al. 2021

Complexity

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“…Therefore, gather prediction can greatly help the city government react in advance and significantly reduce the losses and risks caused by vicious gatherings [1,2]. As an indispensable part of smart transportation, gather prediction mainly leverages the trajectory data collected by various Internet of Things sensing devices [3][4][5][6] (such as mobile phones [7], cars, and other GPS devices [8]). These trajectory data include multiple types of patterns such as walking, driving, and public transportation.…”

Section: Introductionmentioning

confidence: 99%

A Road Network Enhanced Gate Recurrent Unit Model for Gather Prediction in Smart Cities

Yuan

Tian

Yan

et al. 2021

Wireless Communications and Mobile Computing

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Gather prediction is an indispensable part of smart city projects. The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real-time trajectory data and requests stronger spatial-temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real-time condition in a certain city and conducts the gathering situations by predicting users’ aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real-world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.

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“…Physical unclonable functions (PUFs) are new low-cost hardware security primitive with a comparatively simple design ideal for smart and energy-constrained IoT devices. Recent research has proposed to use PUF for low-cost IoT authentications and encryption schemes [4]- [6]. However, the secret keys generated by the proposed PUF may suffer from reliability issues which negatively impact the security and trust in IoT applications.…”

Section: Introductionmentioning

confidence: 99%

“…Different types of sPUFs designs have been proposed as lightweight hardware-based security solutions for IoT applications, detection of physical attacks, i.e. physical tampering and reverse engineering, and hardware Trojan attacks [1]- [6], [15], [24]- [29]. Further, sPUFs have recently emerged as promising hardware-based security primitive to protect consumer electronic devices against invasive and non-invasive attacks.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Lightweight Configurable PUF for Robust IoT Hardware-Assisted Security

et al. 2021

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Lightweight physical unclonable functions (LPUFs) exploit manufacturing process variations of semiconductor integrated circuits (ICs) to protect IoT-based electronic and smart devices from new cyberattacks. This paper proposes two novel security techniques to enhance the robustness of LPUFs using configurable-based ring oscillator PUFs (CF-ROPUFs). These techniques are the intar-die frequency aware (IFA) approach to improve PUF reliability and the logarithmic gamma function (Ln) technique to enhance PUF randomness. The lightweight CF-ROPUF design is realized on hardware, and data samples are collected under varying temperatures and supply voltages over a population of 30 Spartan-3E FP-GAs.Experimental results of the IFA technique in terms of average Hamming Weight (HM) demonstrate that the percentage of the reliable RO sample frequencies PUF output is 98.5%. For the analysis, PUF reliability is evaluated in terms of accuracy, repeatability, and reproducibility, which are the international organization for Standardization (ISO) standards. The results indicate that the RO samples are accurately measured from the CF-ROPUFs mapped in all the chips. After using the proposed 1-out-of-r coding algorithm, the results demonstrate high average repeatability of 98.2% and a magnified average reproducibility of 99.63%. It is also shown that our CF-ROPUF design is immune from accelerated aging impacts reliability issues. Statistical results show thatLnenhances the normality and mitigates the negative impacts of the systematic process variations on RO sample frequencies. Randomness results show that CF-ROPUF binary response bits can successfully pass the 15 NIST test suites for true randomness with an enhanced percentage, 93.3%, with the application of the 1-out-of-r coding INDEX TERMS Lightweight hardware security, configurable ROPUF, PUF reliability, PUF aging, ISO standards.

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ePUF: A lightweight double identity verification in IoT

Cited by 26 publications

References 31 publications

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

A Road Network Enhanced Gate Recurrent Unit Model for Gather Prediction in Smart Cities

Enhancing the Performance of Lightweight Configurable PUF for Robust IoT Hardware-Assisted Security

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