Research on Intrusion Detection System Based on Pattern Recognition

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. This is a typical problem of the classification, so intrusion detection (ID) can be seen as a pattern recognition problem. In this paper, In this paper, we build the intrusion detection system using Adaboost, a prevailing machine learning algorithm, construction detection classification. In the algorithm, decision RBF neural network are used as weak classifiers. For the training sets is multi-attribute, non-linear and massive, we use pattern recognition method of non-linear data dimension reduction algorithm—Isomap algorithm to feature extraction and to improve the speed and training for the handling of classified speed. In the feature extraction after the feature of the dimension and Adaboost algorithm training rounds, were studied and experimented. Finally,the experiment proved that Isomap and Adaboost combination of testing the effectiveness of the mothod.