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