Efficient Intrusion Detection Based on Fuzzy WAR with Genetic Network Programming Using Probability Density Function

In conventional network security relies on mathematics cryptosystem and low counter security measures to taken to prevent Intrusion detection System, although most of this approaches in terms of theoretically impossible to implement. One of the Evolutionary optimization techniques like Genetic Network Programming (GNP) is node based directed graph structures instead of generating a large number of rules and patterns, In this paper focusing on generalize the problem embedded in GNP with association rule mining and address to a issues in IDS and gives a solution to detecting intrusion. Our proposed method follows an Apriori algorithm based fuzzy WAR and GNP and avoids pre and post processing thus eliminating the extra steps during rules generation. This method can sufficient to evaluate misuse and anomaly detection. Experiments on KDD99Cup and DARPA98 data show the high detection rate and accuracy compared with other conventional method.