Enhancing network traffic prediction and anomaly detection via statistical network traffic separation and combination strategies

Jiang, J; Papavassiliou, S

dc.contributor.author	Jiang, J	en
dc.contributor.author	Papavassiliou, S	en
dc.date.accessioned	2014-03-01T01:54:59Z
dc.date.available	2014-03-01T01:54:59Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27543
dc.subject	Anomaly Detection	en
dc.subject	arma model	en
dc.subject	Data Filtering	en
dc.subject	Frequency Domain	en
dc.subject	Moving Average	en
dc.subject	Network Anomaly Detection	en
dc.subject	Network Monitoring	en
dc.subject	Network Traffic	en
dc.subject	Resource Allocation	en
dc.subject	Time Series	en
dc.subject	Traffic Analysis	en
dc.subject	Traffic Prediction	en
dc.subject	Dynamic Threshold	en
dc.subject	Low Frequency	en
dc.subject	Time Dependent	en
dc.title	Enhancing network traffic prediction and anomaly detection via statistical network traffic separation and combination strategies	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.comcom.2005.07.030	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.comcom.2005.07.030	en
heal.publicationDate	2006	en
heal.abstract	In this paper, we propose, study and analyze a new network traffic prediction methodology, based on the ‘frequency domain’ traffic analysis and filtering, with the objective of enhancing the network anomaly detection capabilities. Based on this approach, the traffic can be effectively separated into a baseline component, that includes most of the low frequency traffic and presents low burstiness, and	en
heal.journalName	Computer Communications	en
dc.identifier.doi	10.1016/j.comcom.2005.07.030	en