Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function

In Information Security, intrusion detection is the act of detecting actions that attempt to compromise the security goals. One of the primary challenges to intrusion detection is the problem of misjudgment, misdetection and lack of real time response to the attack. Various data mining techniques as clustering, classification and association rule discovery are being used for intrusion detection. The proposed hybrid technique combines data mining approaches like K Means clustering algorithm and RBF kernel function of Support Vector Machine as a classification module. The main purpose of proposed technique is to decrease the number of attributes associated with each data point. So, the proposed technique can perform better in terms of Detection Rate and Accuracy when applied to KDDCUP’99 Data Set.