An Evolutionary Feature Clustering Approach for Anomaly Detection Using Improved Fuzzy Membership Function: Feature Clustering Approach for Anomaly Detection

Traditionally, IDS have been developed by applying machine learning techniques and followed single learning mechanisms or multiple learning mechanisms. Dimensionality is an important concern which affects classification accuracies and eventually the classifier performance. Feature selection approaches are widely studied and applied in research literature. In this work, a new fuzzy membership function to detect anomalies and intrusions and a method for dimensionality reduction is proposed. CANN could not address R2L and U2R attacks and have completely failed by showing these attack accuracies almost zero. Following CANN, the CLAPP approach has shown better classifier accuracies when compared to classifiers kNN, and SVM. This research aims at improving the accuracy achieved by CLAPP, CANN, and kNN. Experimental results show accuracies obtained using proposed approach is better when compared to other existing approaches. In particular, the detection of U2R and R2L attacks to user accuracies are recorded to be very much promising.