Neurofuzzy and Genetic Network Programming Based Intrusion Detection System

In today’s environment security is the big problem for all kinds of networks. Many methods have been developed to secure the network communication, among them Intrusion Detection System (IDS) is an essential component .The traditional approaches for intrusion detection are currently being used but unable to manage various newly arising attacks and high false alarm rates. Due to the variety of network behaviour and fast development of new attack, it is necessary to develop efficient IDS model to detect all kinds of new attacks. This paper proposes a Network Intrusion Detection model uses the learning algorithm which combines ANN and fuzzy logic and evolutionary optimization technique called Genetic Network Programming (GNP). The proposed model is evaluated using KDDCup99 Dataset which shows higher detection rate as well as low false alarm rate