Apply Blockchain to Overcome Wi-Fi Vulnerabilities

Kim, Seong-Kyu

doi:10.33851/jmis.2019.6.3.139

Cited by 2 publications

(2 citation statements)

References 25 publications

(24 reference statements)

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“…The FRR is the frequency of the system rejecting a true claim of authorization. The FAR is defined as the frequency of the system incorrectly accepting an unauthorized user [38,[46][47][48][49][50][51][52]. The ERR is defined as We employed the true acceptance rate (TAR), the false rejection rate (FRR), the false acceptance rate (FAR), and the equal error rate (EER) [38,[42][43][44][45][46] as performance evaluation measurements.…”

Section: Methodsmentioning

confidence: 99%

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Face Recognition at a Distance for a Stand-Alone Access Control System

Lee

Park

Yoo

et al. 2020

Sensors

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Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

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Section: Methodsmentioning

confidence: 99%

“…The FRR is the frequency of the system rejecting a true claim of authorization. The FAR is defined as the frequency of the system incorrectly accepting an unauthorized user [38,[46][47][48][49][50][51][52]. The ERR is defined as the error rate when FAR and FRR are equal for a particular threshold [44].…”

Section: Methodsmentioning

confidence: 99%

Face Recognition at a Distance for a Stand-Alone Access Control System

Lee

Park

Yoo

et al. 2020

Sensors

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Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study

Ray

Skala

2022

Applied Sciences

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Building a widely distributed hotspot network is a very tedious task due to its complexity. Providing security, fully distributed network services, and a cost-conscious impact are the major challenges behind this goal. To overcome these issues, we have presented a novel distributed hotspot network architecture with five layers that can provide large-scale hotspot coverage as an assimilated result. Our contributions to this new architecture highlight important aspects. First, scalability can be increased by including many Internet of Things (IoT) devices with sensors and Wi-Fi and/or LoraWAN connectivity modules. Second, hotspot owners can rent out their hotspots to create a distributed hotspot network in which the hotspots can act as an ordinary data gateway, a full-fledged hotspot miner, and a light-weight hotspot miner to earn crypto tokens as rewards for certain activities. Third, the advantages of Wi-Fi and LoraWAN can be seamlessly leveraged to achieve optimal coverage, higher network security, and suitable data transmission rate for transferring sensor data from IoT devices to remote application servers and users. Fourth, blockchain is used to enhance the decentralized behavior of the architecture that is presented here by providing immutability and independence from a centralized regulator and making the network architecture more reliable and transparent. The main feature of our paper is the use of the dew-computing paradigm along with hotspots to improve availability, Internet backhaul-agnostic network coverage, and synchronous update capability, and dew-aware leasing to strengthen and improve coverage. We also discuss the key challenges and future roadmap that require further investment and deployment.

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Apply Blockchain to Overcome Wi-Fi Vulnerabilities

Cited by 2 publications

References 25 publications

Face Recognition at a Distance for a Stand-Alone Access Control System

Face Recognition at a Distance for a Stand-Alone Access Control System

Internet of Things Aware Secure Dew Computing Architecture for Distributed Hotspot Network: A Conceptual Study

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