An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

TL;DR

This paper introduces a two-phase neural network approach to accurately identify untrustworthy Web services based on QoS metrics, significantly improving detection accuracy in QoS management.

Contribution

It presents a novel two-phase neural network model combining feedforward and probabilistic neural networks for effective QoS-based Web service trustworthiness identification.

Findings

Achieved 90.5% identification accuracy.

Outperformed existing approaches in QoS trustworthiness detection.

Demonstrated effectiveness on QoS datasets.

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 QoS levels, 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.