An Intelligent Qos Identification For Untrustworthy Web Services Via Two-Phase Neural Networks

Keywords

Neural network; QoS management; Quality of service(QoS); Untrustworthy web service

Abstract

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoSlevels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.

Publication Date

8-31-2016

Publication Title

Proceedings - 2016 IEEE International Conference on Web Services, ICWS 2016

Number of Pages

139-146

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICWS.2016.26

Socpus ID

84991018062 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84991018062

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