TY - JOUR
T1 - Network Anomaly Detection using Fuzzy Gaussian Mixture Models
AU - Tran, Dat
AU - Ma, W
AU - Sharma, Dharmendra
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - fuzzy gaussian mixture model
KW - network anomaly detection
UR - https://www.mendeley.com/catalogue/545feb99-f195-304e-8235-305ee6cdb53a/
M3 - Article
SP - 37
EP - 42
JO - International Journal of Future Generation …
JF - International Journal of Future Generation …
ER -