Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments

Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments

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 accept...

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Bibliographic Details
Main Authors: Smrithy G S, Ramadoss Balakrishnan
Format: Article
Language:English
Published: Croatian Communications and Information Society (CCIS) 2019-09-01
Series:Journal of Communications Software and Systems
Subjects:
Algorithm
Automated Modeling
Cloud Data Center
Data Stream
Non-Parametric Statistical Technique
Online Anomaly Detection
Online Access:https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/717/pdf
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spelling doaj-180e7e1d989d4b408a0fdc12bb26c4df2020-11-25T02:45:41ZengCroatian Communications and Information Society (CCIS)Journal of Communications Software and Systems1845-64211846-60792019-09-01153225232 Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud EnvironmentsSmrithy G SRamadoss BalakrishnanThe 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).https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/717/pdfAlgorithmAutomated ModelingCloud Data CenterData StreamNon-Parametric Statistical TechniqueOnline Anomaly Detection
collection DOAJ
language English
format Article
sources DOAJ
author Smrithy G S
Ramadoss Balakrishnan
spellingShingle Smrithy G S
Ramadoss Balakrishnan
Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments
Journal of Communications Software and Systems
Algorithm
Automated Modeling
Cloud Data Center
Data Stream
Non-Parametric Statistical Technique
Online Anomaly Detection
author_facet Smrithy G S
Ramadoss Balakrishnan
author_sort Smrithy G S
title Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments
title_short Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments
title_full Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments
title_fullStr Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments
title_full_unstemmed Automated Modeling of Real-Time Anomaly Detection using Non-Parametric Statistical technique for Data Streams in Cloud Environments
title_sort automated modeling of real-time anomaly detection using non-parametric statistical technique for data streams in cloud environments
publisher Croatian Communications and Information Society (CCIS)
series Journal of Communications Software and Systems
issn 1845-6421
1846-6079
publishDate 2019-09-01
description 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).
topic Algorithm
Automated Modeling
Cloud Data Center
Data Stream
Non-Parametric Statistical Technique
Online Anomaly Detection
url https://jcomss.fesb.unist.hr/index.php/jcomss/article/view/717/pdf
work_keys_str_mv AT smrithygs automatedmodelingofrealtimeanomalydetectionusingnonparametricstatisticaltechniquefordatastreamsincloudenvironments
AT ramadossbalakrishnan automatedmodelingofrealtimeanomalydetectionusingnonparametricstatisticaltechniquefordatastreamsincloudenvironments
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