Authentication-based Access Control and Data Exchanging Mechanism of IoT Devices in Fog Computing Environment

Pallavi, K N; Kumar, Vivek

doi:10.1007/s11277-020-07834-w

Cited by 23 publications

(11 citation statements)

References 26 publications

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“…This taxonomy process produces the following eight categories, are shown in Figure 3 with their included number of publications: Authentication : [ 12 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , ...…”

Section: Taxonomy and Trendsmentioning

confidence: 99%

“…Authentication : [ 12 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ,...…”

Section: Taxonomy and Trendsunclassified

“…Cloud : [ 24 , 29 , 37 , 38 , 39 , 41 , 56 , 57 , 59 , 60 , 63 , 70 , 71 , 75 , 83 , 85 , 86 , 87 , 94 , 95 , 99 , 103 , 117 , 131 , 132 , 133 , 134 , 135 , 136 , 137 ]…”

Section: Taxonomy and Trendsunclassified

“…There may be instances where either a U2M or M2M model exists in a researched technology. For these instances, a M2M tool can be transformed to work with a U2M model [ 81 ], or the tool can exist in both models [ 33 , 38 , 56 , 74 , 111 ].…”

Section: Details On Goal-based Taxonomy Perspectivesmentioning

confidence: 99%

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Systematic Review of Authentication and Authorization Advancements for the Internet of Things

Trnka

Abdelfattah

Shrestha

et al. 2022

Sensors

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Technologies for the Internet of Things (IoT) are maturing, yet no common standards dictate their direction, leaving space for a plethora of research directions and opportunities. Among the most important IoT topics is security. When we design a robust system, it is important to know the available options for facing common tasks related to access control, authentication, and authorization. In this review, we systematically analyze 1622 peer-reviewed publications from October 2017 to December 2020 to find the taxonomy of security solutions. In addition, we assess and categorize current practices related to IoT security solutions, commonly involved technologies, and standards applied in recent research. This manuscript provides a practical road map to recent research, guiding the reader and providing an overview of recent research efforts.

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Section: Taxonomy and Trendsmentioning

confidence: 99%

Section: Taxonomy and Trendsunclassified

“…Cloud : [ 24 , 29 , 37 , 38 , 39 , 41 , 56 , 57 , 59 , 60 , 63 , 70 , 71 , 75 , 83 , 85 , 86 , 87 , 94 , 95 , 99 , 103 , 117 , 131 , 132 , 133 , 134 , 135 , 136 , 137 ]…”

Section: Taxonomy and Trendsunclassified

Section: Details On Goal-based Taxonomy Perspectivesmentioning

confidence: 99%

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Systematic Review of Authentication and Authorization Advancements for the Internet of Things

Trnka

Abdelfattah

Shrestha

et al. 2022

Sensors

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“…Security in IoT has become a challenging task due to the comprehensive fewer hardware possessions, comprehensive openness, and the heterogeneity of IoT systems [19] [5] [6] [1]. Fog computing facilitates the service in the Cloud things at the network edges, which shows that the data dispensation and the communication can be achieved very close to data sources [22] [23] [24] [11] [2]. It is an encouraging framework to deal with the low latency and geographical distribution desired in the IoT environment.…”

Section: Introductionmentioning

confidence: 99%

Attack detection in IoT-Fog computing environment using hybrid optimization-based Generative Adversarial Network

Pallavi¹,

Kumar²

2022

Preprint

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Internet of Things (IoT) is considered the rapidly growing paradigm in the evolution of computing. A number of attack detection methods is introduced for detecting the attacks in IoT-Fog computing, but achieving significant result poses a complex task due to the requirements of the IoT devices, like distribution, low latency, limitation of resources, and scalability. Hence, an effective attack detection method is designed using the proposed Sail Fish Cat Spider-based Generative Adversarial Network for detecting the attacks in the IoT framework. The proposed SFCS is developed by the integration of Sail Fish Optimization (SFO) with Cat Swarm Optimization (CSO) and Spider Monkey Optimization (SMO), respectively. Here, the features are selected from the data using Minkowski distance, and the process of attack detection is accomplished using GAN. However, the deep learning classifier named GAN is trained using the proposed SFCS optimization algorithm. The deep learning classifier is more effective in detecting cyber-attacks in the IoT environment. However, the average values of the proposed SFCS-based GAN obtained by considering the metrics, recall, precision, and F-measure is specified with the values of 85.787%, 85.165%, and 85.247% for DoS attack, and recall, precision, and F-measure of 85.340%, 85.653%, and 85.243% for Fuzzers attack.

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Security analysis of fog computing environment for ensuring the security and privacy of information

Gola,

Arya,

Khan

et al. 2023

Trans Emerging Tel Tech

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In fog computing, the management, measurement, configuration, control, communication, and storage tasks are carried out collaboratively by one or more near‐user or end users. Fog computing is widely utilized in major application domains like centralized fog utilized in a shopping complex (UXFog), agriculture domain (FoAgro), health‐care domain (MediFog), vehicle parking system, and energy Lattices. The execution of fog computing at the system's edge provides the elevated quality of service (QoS), promotes area mindfulness, and minimal dormancy for the continuous and online streaming application areas. Fog networking is extremely concerned about wireless system security due to the prevalence of wireless communication in fog computing. Sniffer attacks, Jamming assaults, and several other intrusions are considered examples of security issues. While considering network storage issues, factors like interaction management, dynamic workload, and low dormancy are the most challenging aspects of designing the network model. Thus, privacy and security issues are crucial while designing the network model, accomplished through intrusion detection, access control and authentication methods. Therefore, the security analysis of fog computing based on authentication, access control and intrusion detection mechanisms are analyzed in this research to design a novel technique with enhanced security and privacy features for sharing and accessing information.

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Authentication-based Access Control and Data Exchanging Mechanism of IoT Devices in Fog Computing Environment

Cited by 23 publications

References 26 publications

Systematic Review of Authentication and Authorization Advancements for the Internet of Things

Systematic Review of Authentication and Authorization Advancements for the Internet of Things

Attack detection in IoT-Fog computing environment using hybrid optimization-based Generative Adversarial Network

Security analysis of fog computing environment for ensuring the security and privacy of information

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