@Article{cmc.2020.010394,
AUTHOR = {Muhammad Hasnain, Seung Ryul Jeong, Muhammad Fermi Pasha, Imran Ghani},
TITLE = {Performance Anomaly Detection in Web Services: An RNN- Based Approach Using Dynamic Quality of Service Features},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {729--752},
URL = {http://www.techscience.com/cmc/v64n2/39327},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010394}
}