Fuzzy Rule-Based Expert System for Determining Trustworthiness of Cloud Service Providers

Chahal, Rajanpreet Kaur; Singh, Sarbjeet

doi:10.1007/s40815-016-0149-1

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…• Rule-based: Using conditions to evaluate given information for generating relevant knowledge regarding the trust of an entity is treated as a rule-based solution. The rule-based approach utilizes any type of architecture; however, it always has dominance for deciding rules or a consensus model for arriving at a common decision while formulating principles of trust evaluations [238], [239].…”

Section: B Decision-basedmentioning

confidence: 99%

Security, Privacy and Trust for Smart Mobile- Internet of Things (M-IoT): A Survey

et al. 2020

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With an enormous range of applications, the Internet of Things (IoT) has magnetized industries and academicians from everywhere. IoT facilitates operations through ubiquitous connectivity by providing Internet access to all the devices with computing capabilities. With the evolution of wireless infrastructure, the focus from simple IoT has been shifted to smart, connected and mobile IoT (M-IoT) devices and platforms, which can enable low-complexity, low-cost and efficient computing through sensors, machines, and even crowdsourcing. All these devices can be grouped under a common term of M-IoT. Even though the positive impact on applications has been tremendous, security, privacy and trust are still the major concerns for such networks and insufficient enforcement of these requirements introduces non-negligible threats to M-IoT devices and platforms. Thus, it is important to understand the range of solutions which are available for providing a secure, privacy-compliant, and trustworthy mechanism for M-IoT. There is no direct survey available, which focuses on security, privacy, trust, secure protocols, physical layer security and handover protections in M-IoT. This paper covers such requisites and presents comparisons of statethe-art solutions for IoT which are applicable to security, privacy, and trust in smart and connected M-IoT networks. Apart from these, various challenges, applications, advantages, technologies, standards, open issues, and roadmap for security, privacy and trust are also discussed in this paper.

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Section: B Decision-basedmentioning

confidence: 99%

Security, Privacy and Trust for Smart Mobile- Internet of Things (M-IoT): A Survey

et al. 2020

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“…Chahal and Singh have proposed an expert system based on a fuzzy rule that estimates the trustworthiness of cloud service providers. The data possessed from the review of four parameters (security, usability, reliability, and performance) of five cloud service providers perform as the data set to estimate public review and direct trust.…”

Section: Related Workmentioning

confidence: 99%

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

Zanbouri

Navimipour

2019

Int J Communication

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Summary One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.

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“…Chahal and Singh [19] have presented a fuzzy rule based expert system for the determination of trustworthiness of cloud service providers. Direct‐Trust, Public Review, and Auditor‐Trust were combined to find the trustworthiness.…”

Section: Related Workmentioning

confidence: 99%

Efficient multi‐objective particle swarm optimisation based ranking system for cloud service selection

Kunhambu

Balan

2019

IET Communications

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Fuzzy Rule-Based Expert System for Determining Trustworthiness of Cloud Service Providers

Cited by 17 publications

References 13 publications

Security, Privacy and Trust for Smart Mobile- Internet of Things (M-IoT): A Survey

Security, Privacy and Trust for Smart Mobile- Internet of Things (M-IoT): A Survey

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

Efficient multi‐objective particle swarm optimisation based ranking system for cloud service selection

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