The Research Of FSVM Intrusion Detection Algorithom Based On Inverted Binary Tree

SVM is a machine learning method based on statistical learning theory, which combines the maximum interval and the kernel function theory together and effectively solves the problems of small sample size, high dimension, nonlinear, over learning, local optimal solution and so on.With the development of network technology and the expansion of the scope of application, network is becoming the main method of information processing. Information systems have become a infrastructure of a country and network security has become an important component of the national security. Intrusion detection technology is an important dynamic security protection technology, and has become an important research direction of the information technology. Intrusion detection is essentially a pattern recognition problem, the applications of support vector machine in intrusion detection can improve the detection performance.Based on the study on intrusion detection system and support vector machines, we do the following work and innovation:1. A new feature selection algorithm to intrusion detection data is proposed.It's that using ReliefF algorithm to remove irrelevant features, and then remove the redundant features using KNN algorithm;2. A new binary tree structure is proposed to solve the "error accumulation" problem; A new criterion of class separation based on Kernel function is proposed.It has the better effective to reflect the separability and has a better geometric significance;3. Considering the advantages of support vector machines based on binary tree and fuzzy support vector machine, a fuzzy factor is introduced to multi-class support vector machine based on binary tree; a new fuzzy membership function is proposed based on class center distance and the KNN algorithm to distinguish support vector and the noises more effectively.