Network Anomaly Detection using Fuzzy Gaussian Mixture Models

Abstract

Fuzzy Gaussian mixture modeling method is proposed in this paper for network anomaly detection. A mixture of Gaussian distributions was used to represent the network data in multi-dimensional feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the KDD Cup data set. Experimental results have shown that the proposed method is more effective than the vector quantization method.