Network attacks identification using consistency based feature selection and self organizing maps

Anomaly detection is one of the major areas of research with the tremendous development of computer networks. Any intrusion detection model designed should have the ability to visualize high dimensional data with high processing and accurate detection rate. Integrated Intrusion detection models combine the advantage of low false positive rate and shorter detection time. Hence this paper proposes an anomaly detection model by deploying consistency based feature selection, J48 decision tree and self organizing map (SOM). Experimental analysis has been carried on KDD99 data set and each of the features selected using the integrated mechanism has been able to identify the attacks in the data set.