Development of trust based access control models using fuzzy logic in cloud computing

Kesarwani, Abhishek; Khilar, Pabitra Mohan

doi:10.1016/j.jksuci.2019.11.001

Cited by 30 publications

(18 citation statements)

References 25 publications

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“…Abhishek Kesarwani and Pabitra Mohan Khiler (2022), [1] have proposed the development of trust based access control models using fuzzy logic in cloud computing. In this model the trust value is calculated for both cloud users and cloud service providers.…”

Section: Literature Surveymentioning

confidence: 99%

“…The linguistic variables need to be assigned for the membership function and used in specifying the rules. The linguistic variables hold the value between zero (0) to one (1). The 0 value is the least value and 1 is the highest value where the performance value is good.…”

Section: If-then Rulesmentioning

confidence: 99%

“…The above Fig. 2 depicts the architecture of Fuzzy Inference System [1] which accepts the crisp inputs and generates the crisp outputs with a lot of internal processing. The Fuzzifier component accepts the crisp inputs and converts it into the fuzzy input sets and is passed to the inference engine.…”

Section: If-then Rulesmentioning

confidence: 99%

See 2 more Smart Citations

Trust Model for Cloud and Grid Environment Using Fuzzy Logic and Artificial Neural Network

Kumar¹,

S.²,

Gyani³

2022

INDJCSE

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Cloud Computing is a technology which offers various on-demand services such as software, platform, and infrastructure required by the clients. Grid Computing is a process of allotting the resources required to solve complex problems. In cloud or grid environment the service providers provides the services/resources to the clients. In this regard the service providers need to maintain and manage trust for further proceedings. This research article proposed a trust model using Fuzzy Logic and Artificial Neural Network (ANN) applicable for both cloud and grid environment. The Fuzzy Logic is used to remove the impreciseness of the data. The decision derived by using Fuzzy Logic lies between zero (0) to one (1) that represents the continuous values, where 0 is the lowest and 1 is the highest value in decision making. In this article Fuzzy Logic is used to generate the trust value based on the fuzzy rules by accepting the given inputs of six parameters namely reliability, response time, fault tolerance, security, elasticity, and service level agreement (SLA). Then based on the generated trust value the service provider is allotted to the clients. Artificial Neural Network is a prediction model used to predict the trust value of the service provider based on the same six parameters mentioned as input for Fuzzy Logic. Based on the predicted trust value the service provider is allotted to the client. This article shows the experiments and results based on the Fuzzy Logic Model and Artificial Neural Netwok Model for the process of service provider allottment. The experiments and results worked on the dataset of 20000 instances collected from the authenticated source.

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Section: Literature Surveymentioning

confidence: 99%

Section: If-then Rulesmentioning

confidence: 99%

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Trust Model for Cloud and Grid Environment Using Fuzzy Logic and Artificial Neural Network

Kumar¹,

S.²,

Gyani³

2022

INDJCSE

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“…A trustor establishes trust according to the belief, performance, or behavior. A trustee will use the product provided by them with the level of belief, which will not match the trustor's trust [105]. The following diagram of figure 5 displays the classification of trust.…”

Section: Classification Of Cloud Trust Modelmentioning

confidence: 99%

Untitled

2021

JESTR

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The emergence of the cloud computing paradigm has influenced business organizations for migration over two decades. This has enabled a more incredible communication speed paired with a better fault tolerance of web-based applications and the scope of having practically unlimited storage available for the user and business data. Such colossal data availability over cloud platforms has increased concerns about the security of the same from various threats such as unauthorized eavesdropping, deliberate malicious mutation of data, loss of data integrity, and identity theft. The presence of such security vulnerabilities has rendered cloud security to be an active research area. This article aims to deep dive into the present stateof-the-art in cloud security research and mitigation approaches. It is observed that cloud storage, cloud services are the widely used applications among the organization. However, vulnerabilities and security challenges faced by this two area is maximum. Besides, outsourcing sensitive information to cloud service provider's (CSP) end leads to trust issues among organizations. This article performs a detailed comparative analysis of the security frameworks developed towards cloud storage level, cloud service level, and cloud trust level to date and throws light on future research directives. All the existing research has been evaluated against CIA (confidentiality, Integrity, Availability) principles here.

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“…Es a partir de uso de un lenguaje natural que son introducidas reglas a un controlador, refiriéndose a sistemas de control, o computadora para que éstos imiten la forma del razonamiento humano, en el cual, la esencia del análisis sobre una característica de interés es lingüística y no matemática (Zadeh, 1999). Así, un sistema de inferencia difuso emplea tales variables lingüísticas para conformar una base de conocimiento que permite discernir un comportamiento de salida, dados ciertos parámetros de entrada; ambos con una pertenencia no absoluta a estos conjuntos (Kesarwani & Khilar, 2019).…”

Section: Introductionunclassified

Sintonización genética de sistema de inferencia difuso aplicado al control de posición en un motor de corriente directa

Rodríguez-Franco

Delgado-Guerrero

López-Álvarez

et al. 2020

JSI

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This paper proposes the development and application of a genetic algorithm to improve the function of a fuzzy inference machine used in the shaft position control of a direct current motor. In this first stage, the study is executed on system leaving out the transfer function that defines the dynamic characteristics of the mentioned actuator, which leads to the subsequent physical verification of its behavior. The analysis of the calculated error is considered as the difference between the desired position and the current position reached by motor shaft, as well as the signal that the inference system will generate to correct the given magnitude of error. Likewise, the theoretical foundations that support this application and the methodology executed to obtain the results that confirm the effectiveness in the use of the genetic algorithm as a means of tuning the fuzzy inference system developed are described, when describing a global error lower than 20% in the control action deducted with respect to the one desired.

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Development of trust based access control models using fuzzy logic in cloud computing

Cited by 30 publications

References 25 publications

Trust Model for Cloud and Grid Environment Using Fuzzy Logic and Artificial Neural Network

Trust Model for Cloud and Grid Environment Using Fuzzy Logic and Artificial Neural Network

Untitled

Sintonización genética de sistema de inferencia difuso aplicado al control de posición en un motor de corriente directa

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