Research On Key Technologies In Intrusion Detection Based On Pattern Recognition

With the rapid development and wide application of network technologies, network security is becoming more and more important. It is a very urgent problem in intrusion detection system that how to recognize existing attacks and increasingly new attacks rapidly, exactly and effectively. The intrusion detection technology based on pattern recognition or intelligence method has been developing rapidly and yielded encouraging effects since Dr. Sandeep Kumar introduced pattern recognition technology into the network intrusion detection. Compared to traditional intrusion detection technologies, those based on pattern recognition have the advantages of high detection accuracy and the ability to recognize many new types of attacks. However, these methods have high computing complexity and fail to meet the real-time demand. In this thesis, we present an intrusion detection model based on BP neuron network classifier. It is necessary to reduce both the number of features and instances of training data input into the neuron network classifier, due to the huge amount of dimensions (features) and instances. By doing this, the computing cost of the intrusion detection system based on pattern recognition can be decreased significantly. To this end, the following key issues are studied in this thesis:1. Feature extraction and feature selection of training dataWe extract and select the features of input training data in order to...