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Black Box Anomaly Detection in Multi-Cloud Environment

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Mahendra Kumar Ahirwar, Manish Kumar Ahirwar, Uday Chourasia

Mahendra Kumar Ahirwar, Manish Kumar Ahirwar and Uday Chourasia. Black Box Anomaly Detection in Multi-Cloud Environment. International Journal of Computer Applications 144(2):31-37, June 2016. BibTeX

	author = {Mahendra Kumar Ahirwar and Manish Kumar Ahirwar and Uday Chourasia},
	title = {Black Box Anomaly Detection in Multi-Cloud Environment},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {2},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {31-37},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2016910125},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Automatic identification of anomalies for performance diagnosis in the cloud computing is a fundamental and challenging issue. TPA is interested to identifies these anomalies and remove them so that the performance of the cloud systems increased. In this paper we are proposing an Automatic Black Box Anomaly Detector which can find anomalies automatically with minimum human intervention. Using this detector we can find old and even new anomalies created in the cloud computing systems even if we don’t have knowledge of source code (i.e. black box testing). Automatic black box anomaly detection is a two step process in which first of all data from different sources is collected and transform it into a common form that is act as input for black box anomaly detector and secondly anomaly detection is performed.


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Black box anomaly detector, cloud service provider, performance diagnosis, cloud systems.