An Efficient Model for Network Intrusion Detection System based on an Evolutionary Computational Intelligence Approach

Intrusion Detection systems are increasingly a key part of system defence. Various approaches to Intrusion Detection are currently being used but false alarm rate is higher in those approaches. Network Intrusion Detection involves differentiating the attacks like DOS, U2L, R2L and Probe from the Normal user on the internet. Due to the variety of network behaviors and the rapid development of attack fashions, it's necessary to develop an efficient model to detect all kinds of attacks. Building an effective IDS is an enormous knowledge engineering task. Characteristics of computational intelligence systems such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion model. In this paper, we propose a network intrusion detection model based on evolutionary optimization technique called Genetic Network Programming (GNP) with sub attribute utilization mechanism. The proposed model is evaluated