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A Model for Trust Decision, Data Analysis, and Evaluation to Identify Quality Web Services

A Model for Trust Decision, Data Analysis, and Evaluation to Identify Quality Web Services
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Author(s): Shobhana Kashyap (National Institute of Technology, Jalandhar, India)and Avtar Singh (National Institute of Technology, Jalandhar, India)
Copyright: 2024
Pages: 27
Source title: Improving Security, Privacy, and Trust in Cloud Computing
Source Author(s)/Editor(s): Pawan Kumar Goel (Raj Kumar Goel Institute of Technology, India), Hari Mohan Pandey (Bournemouth University, UK), Amit Singhal (Raj Kumar Goel Institute of Technology, India)and Sanyam Agarwal (ACE Group of Colleges, India)
DOI: 10.4018/979-8-3693-1431-9.ch011

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Abstract

Cloud computing has emerged as a powerful paradigm for delivering web services, and includes scalability, flexibility, and cost efficiency. Due to functional overlap and diversity, web services form a major challenge for selecting adequate services to develop user-provider trust. To address the issue, this study presented a machine learning based trusted model to assist users in selecting trustworthy web services. In the initial stage, using K-Means clustering method the services are selected based on three clusters such as high, medium, and low trust. Next, the trust score is generated by evaluating performance parameters to identify the best services. Experiments conducted with QWS datasets demonstrate that the proposed approach efficiently predicts adequate services with a minimum error rate and high accuracy gain. This technique achieves a 99.32%, 99.36% and 99.48% accuracy rates for the low, medium, and high trust prediction, respectively. The result shows that it is more effective than existing approaches and builds a strong trust relation between users and providers.

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