Paper Title:
A Novel Network Traffic Anomaly Detection Based on Multi-Scale Fusion
  Abstract

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, D. N. Cheng, H. Lei, "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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