A new method for evaluating cloud computing user behavior trust

Hosseini, Seyedeh Bahare; Shojaee, Aida; Agheli, Nafiseh

doi:10.1109/ikt.2015.7288735

Cited by 15 publications

(3 citation statements)

References 6 publications

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“…Paul et al [31] proposed a trust-based access control model in a cloud environment, which uses role-based access control policies for users and modifies them based on their trust level to achieve dynamic trust evaluation. Hosseini et al [32] used decision trees, logistic regression, plain Bayesian, and various other machine learning methods of trust evaluation strategies to predict the trust values of users and resources. Yang et al [33] proposed an integrated model-based trust evaluation method for user behavior, namely ordered logit regression, by combining the advantages of statistical methods and hierarchical analysis.…”

Section: User Trust Evaluationmentioning

confidence: 99%

A Dynamic and Fine-Grained User Trust Evaluation Model for Micro-Segmentation Cloud Computing Environment

Chaoqun Kang,

Erxia Li

et al. 2023

Journal of Computers

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<p>With the diversity and complexity of user access behaviors in the “micro-segmentation” cloud computing environment, it is no longer possible to control unauthorized access of authorized users by only relying on user identity login authentication to control user access to cloud resources. The existing trust evaluation methods can not cope with the characteristics of “micro-isolated” cloud environment, which is characterized by high granularity of resources, increasing number of users’ access requests and rapid changes. Based on the zero-trust principle of “Never trust, al-ways verify”, we propose a dynamic, fine-grained user trust evaluation model for micro-segmentation cloud computing environment, which combines multiple user trust attributes and leverages the subjective-objective approach to assign weights to trust attribute indicators to achieve dynamic scoring of users’ real-time behaviors. To capture the characteristics of users’ intrinsic behaviors, we use correlation analysis to identify the correlation between users’ current and historical behaviors, and combine sliding windows and penalty functions to optimize the model. The massive simulation experiments demonstrate the effectiveness of the proposed dynamic and fine-grained method, which can effectively combine the intrinsic correlation of users’ own access behavior and the difference of access behavior among different users.</p> <p> </p>

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Section: User Trust Evaluationmentioning

confidence: 99%

A Dynamic and Fine-Grained User Trust Evaluation Model for Micro-Segmentation Cloud Computing Environment

Chaoqun Kang,

Erxia Li

et al. 2023

Journal of Computers

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“…Such as the fuzzy logic technique [8] based on cloud computing, and the mutual trust based access control model [9] combined with trust management. What's more, the paper [10] proposed a dynamic trust evaluate method to deal with cloud user's behavior by using entropy method and AHP, and the paper [11] proposed a new method for user behavior trust evaluation that considers user's identification code and mac address to prevent the registration of members with multiple user names and makes available the information of user behavior trust in other cloud computing environments.…”

Section: Stage Of the Researchmentioning

confidence: 99%

Real-time Evaluation Mechanism Based on Double Evidence Classification of User Behavior

Zhang¹,

Zhang²,

Zhang³

2016

IJSIA

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“…Li et al proposed the concept of direct trust, indirect reputation, the weights of trust evaluation indexes with the inherent characteristics for the cloud manufacturing service platform, and defined a comprehensive assessment model [10]. Hosseini et al considered that the tenants could manipulate the software, operating systems or even network infrastructure provided by the cloud vendors directly, and the credibility of tenants' behavior would directly affect the credibility of systems [11]. They established an evaluation system of tenant behaviors including credible layer, sub-credible layer and credible evidence layer in the cloud environment.…”

Section: Pos(iscc2015)064mentioning

confidence: 99%

Trust Evaluation Method for the Perceptive Credibility of Tenants in the Cloud Platform

Wang¹

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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The malicious usage of the cloud platform, untrusted providers, and untrusted perception from the users all contribute to the difficult application of the cloud services. In order to solve these issues, a trust evaluation method of the cloud service based on user perception is proposed. Based on fuzzy set theory, the behaviors of nodes in the cloud platform are quantitated, and the trust values of the services provider are measured; furthermore, an enhanced assessment mechanism in consideration of tenants' perceived credibility is studied, the fuzzy clustering algorithm is used to match the resource and task, and the hierarchical access control policy is deployed based on the credibility of the tenants; finally, a comprehensive assessment of the cloud services is integrated with the above three aspects of the evaluation. Analysis results show that the proposed method can filter malicious services in the cloud, match the optimal resource precisely, and prevent important data from being leaked to untrusted tenants. Experiments results further indicate the excellent performance of the proposed method. The proposed method can characterize the risk of the cloud platform and meet the diverse trusted needs of the cloud platform from varied tenants.

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A new method for evaluating cloud computing user behavior trust

Cited by 15 publications

References 6 publications

A Dynamic and Fine-Grained User Trust Evaluation Model for Micro-Segmentation Cloud Computing Environment

A Dynamic and Fine-Grained User Trust Evaluation Model for Micro-Segmentation Cloud Computing Environment

Real-time Evaluation Mechanism Based on Double Evidence Classification of User Behavior

Trust Evaluation Method for the Perceptive Credibility of Tenants in the Cloud Platform

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