Optimize Intrusion Prevention and Minimization of Threats for Stream Data Classification

In Stream data classification intrusion detection happens when a completely new kind of attack occurs in the traffic. Novel class detection approach solves the problem of intrusion detection based on ensemble technique of clustering and classification on feature evaluation technique. Feature evolution process faced a problem of exact selection of cluster midpoint for the process of clusters which are in different grouped. Here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of classes. Feature evolution processes for GA are discussed in details and implemented. Feature evolution theory to information to filter the Stream data and thus reduce the complexity. We used the KDD99 benchmark dataset and obtained reasonable detection rate.