Support Vector Machine-based Network Intrusion Detection

Compared with the traditional statistics, Statistical Learning Theory (SLT) is a theory that specialized in machine learning with finite samples, which provides a firm foundation to support vector machines (SVM),that is considered as a candidate to replace neural networks and other traditional classification methods for its advantage and high generalization ability.However, SVM is a novel learning machine and still has many aspects that needs further research. Because most of research is limited in theory, and lack of applications. Therefore, it is an active promoting that study how to improve the use of support vector machines in intrusion detection can be helpful to the application of Support Vector Machine and development of IDS.This study includes the implementation of training and classification using kdd99 dataset, with LIBSVM software package. A number of classification experiments with IDS dataset are performed and the results are analyzed and compared to improve SVM method. The objective is an effort to detect intrusion with SVM method precisely.