Study On The Intrusion Detection Technique Based On RBF Neural Network

With the fast development of the Internet, the problem about computer network security becomes more and more outstanding. Network security techniques include router filter, firewalls, intrusion detection, audit, counteroffensive and etc. In these techniques, router filter and firewalls are static security techniques, and others are dynamic security techniques. Static security techniques work on preventing invalid access of system, but they probably bring unknown loss when genuine network attacks occur, especially novel network attacks. Therefore, some active network security defense methods and counterattack methods need to be researched. Intrusion Detection System (IDS) is a dynamic security technique, and it has become a crucial method in network security. Presently, the false alarm rate of most IDSs is high and their efficiency is low. Aimed at these drawbacks of present IDSs, researches on IDS models and neural network-based intrusion detection are presented in this paper.An intrusion detection model based on Radial Basis Function Neural Networks is presented. Firstly, arisen background, definition and functions of intrusion detection are introduced in this paper. Some intrusion detection methods, which are often used, are analyzed. The basic principles of neural networks are introduced, and the superiority of neural networks applied in intrusion detection is also analyzed. An intrusion detection model based on Radial Basis Function Neural Networks is proposed in this paper. At the same time, the realization of this model is studied and the kernel part which constitutes the model is analyzed and designed. The function of the model is explained in the theory.Secondly, a solution based on the theory of function approximation is proposed to select the key parameter of neural network. The solution is realized by Generalized Regression Neural Network, and is proved by experiment. The feasibility and deficiencies of this solution are explained by the analysis of the result.Finally, to the trained neural network we made the detection experiment, the result explained that the detection precision and the intrusion detection system capability are efficiently improved by the Radial Basis Function Neural Networks applied in intrusion detection technology.