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
Copyright Status
Unknown
Socpus ID
84991018062 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84991018062
STARS Citation
Wang, Weidong; Wang, Liqiang; and Lu, Wei, "An Intelligent Qos Identification For Untrustworthy Web Services Via Two-Phase Neural Networks" (2016). Scopus Export 2015-2019. 4251.
https://stars.library.ucf.edu/scopus2015/4251