Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments
Abstract
The main objective of online anomaly detection is to identify abnormal/unusual behavior such as network intrusions, malware infections, over utilized system resources due to design defects etc from real time data stream. Terrabytes of performance data generated in cloud data centers is a well accepted example of such data stream in real time. In this paper, we propose an online anomaly detection framework using non-parametric statistical technique in cloud data center. In order to determine the accuracy of the proposed work, we experiments it to data collected from RUBis cloud testbed and Yahoo Cloud Serving Benchmark (YCSB). Our experimental results shows the greater accuracy in terms of True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR) and False Negative Rate (FNR).
Keywords
algorithm, Automated Modeling, Cloud Data Center, Data Stream, Non-Parametric Statistical Technique
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
S. G S and R. Balakrishnan, "Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments," in Journal of Communications Software and Systems, vol. 15, no. 3, pp. 225-232, July 2019, doi: 10.24138/jcomss.v15i3.717
@article{g-s2019automatedmodeling,
author = {Smrithy G S and Ramadoss Balakrishnan},
title = {Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments},
journal = {Journal of Communications Software and Systems},
month = {7},
year = {2019},
volume = {15},
number = {3},
pages = {225--232},
doi = {10.24138/jcomss.v15i3.717},
url = {https://doi.org/10.24138/jcomss.v15i3.717}
}