A Novel Network Traffic Anomaly Detection Based on Multi-Scale Fusion

Abstract:

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Detecting network traffic anomaly is very important for network security. But it has high false alarm rate, low detect rate and that can’t perform real-time detection in the backbone very well due to its nonlinearity, nonstationarity and self-similarity. Therefore we propose a novel detection method—EMD-DS, and prove that it can reduce mean error rate of anomaly detection efficiently after EMD. On the KDD CUP 1999 intrusion detection evaluation data set, this detector detects 85.1% attacks at low false alarm rate which is better than some other systems.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

102-105

DOI:

10.4028/www.scientific.net/AMM.48-49.102

Citation:

G. Z. Cheng et al., "A Novel Network Traffic Anomaly Detection Based on Multi-Scale Fusion", Applied Mechanics and Materials, Vols. 48-49, pp. 102-105, 2011

Online since:

February 2011

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Price:

$35.00

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