Open Access
ARTICLE
Performance Anomaly Detection in Web Services: An RNN- Based Approach Using Dynamic Quality of Service Features
Muhammad Hasnain1, Seung Ryul Jeong2, *, Muhammad Fermi Pasha3, Imran Ghani4
1 School of Information Technology, Monash University, Subang Jaya, 47500, Malaysia.
2 Graduate School of Business IT, Kookmin University, Seoul, Korea.
3 School of Information Technology, Monash University, Subang Jaya, 47500, Malaysia.
4 Department of Mathematical and Computer Sciences, Indiana University of Pennsylvania, Indiana, USA.
* Corresponding Author: Seung Ryul Jeong. Email: .
Computers, Materials & Continua 2020, 64(2), 729-752. https://doi.org/10.32604/cmc.2020.010394
Received 02 March 2020; Accepted 09 April 2020; Issue published 10 June 2020
Abstract
Performance anomaly detection is the process of identifying occurrences that
do not conform to expected behavior or correlate with other incidents or events in time
series data. Anomaly detection has been applied to areas such as fraud detection,
intrusion detection systems, and network systems. In this paper, we propose an anomaly
detection framework that uses dynamic features of quality of service that are collected in
a simulated setup. Three variants of recurrent neural networks-SimpleRNN, long short
term memory, and gated recurrent unit are evaluated. The results reveal that the proposed
method effectively detects anomalies in web services with high accuracy. The
performance of the proposed anomaly detection framework is superior to that of existing
approaches using maximum accuracy and detection rate metrics.
Keywords
Cite This Article
APA Style
Hasnain, M., Jeong, S.R., Pasha, M.F., Ghani, I. (2020). Performance anomaly detection in web services: an RNN- based approach using dynamic quality of service features. Computers, Materials & Continua, 64(2), 729-752. https://doi.org/10.32604/cmc.2020.010394
Vancouver Style
Hasnain M, Jeong SR, Pasha MF, Ghani I. Performance anomaly detection in web services: an RNN- based approach using dynamic quality of service features. Comput Mater Contin. 2020;64(2):729-752 https://doi.org/10.32604/cmc.2020.010394
IEEE Style
M. Hasnain, S.R. Jeong, M.F. Pasha, and I. Ghani "Performance Anomaly Detection in Web Services: An RNN- Based Approach Using Dynamic Quality of Service Features," Comput. Mater. Contin., vol. 64, no. 2, pp. 729-752. 2020. https://doi.org/10.32604/cmc.2020.010394
Citations