Performance Anomaly Detection in Web Services: An RNN- Based Approach Using Dynamic Quality of Service Features |
| |
Authors: | Muhammad Hasnain Seung Ryul Jeong Muhammad Fermi Pasha Imran Ghani |
| |
Affiliation: | 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. |
| |
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: | Point anomaly anomaly detection recurrent neural networks web services simulated data |
|
| 点击此处可从《》浏览原始摘要信息 |
|
点击此处可从《》下载全文 |
|