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A Divergence-measure Based Classification Method for Detecting Anomalies in Network Traffic

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3 Author(s)
Balagani, K.S. ; Louisiana Tech Univ., Ruston ; Phoha, V.V. ; Kuchimanchi, G.K.

We present 'D-CAD,' a novel divergence-measure based classification method for anomaly detection in network traffic. The D-CAD method identifies anomalies by performing classification on features drawn from software sensors that monitor network traffic. We compare the performance of the D-CAD method with two classifier based anomaly detection methods implemented using supervised Bayesian estimation and supervised maximum-likelihood estimation. Results show that the area under receiver operating characteristic curve (AUC) of the D-CAD method is as high as 0.9524, compared to an AUC value of 0.9102 of the supervised maximum-likelihood estimation based anomaly detection method and to an AUC value of 0.8887 of the supervised Bayesian estimation based anomaly detection method.

Published in:

Networking, Sensing and Control, 2007 IEEE International Conference on

Date of Conference:

15-17 April 2007

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