A Real-Time Network Traffic Anomaly Detection Scheme Using NetFlow Data 


Vol. 12,  No. 1, pp. 19-28, Feb.  2005
10.3745/KIPSTC.2005.12.1.19


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  Abstract

Recently, it has been sharply increased the interests to detect the network traffic anomalies to help protect the computer network from unknown attacks. In this paper, we propose a new anomaly detection scheme using the simple linear regression analysis for the exported NetFlow data, such as bits per second and flows per second, from a border router at a campus network. In order to verify the proposed scheme, we apply it to a real campus network and compare the results with the Holt-Winters seasonal algorithm. In particular, we integrate in into the RRDtool for detecting the anomalies in real time.

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[IEEE Style]

K. H. Kang, J. S. Jang, K. Y. Kim, "A Real-Time Network Traffic Anomaly Detection Scheme Using NetFlow Data," The KIPS Transactions:PartC, vol. 12, no. 1, pp. 19-28, 2005. DOI: 10.3745/KIPSTC.2005.12.1.19.

[ACM Style]

Koo Hong Kang, Jong Soo Jang, and Ki Young Kim. 2005. A Real-Time Network Traffic Anomaly Detection Scheme Using NetFlow Data. The KIPS Transactions:PartC, 12, 1, (2005), 19-28. DOI: 10.3745/KIPSTC.2005.12.1.19.

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