In this paper, we present an intrusion detection model based on genetic algorithm and neural network. The key idea is to take advantage of classification abilities of genetic algorithm and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of this System. We used the KDD99 benchmark dataset and obtained reasonable detection rate