Study On Data Mining Based Intrusion Detection For Network

With the wide application of network and other information technologies, the security of network system becomes very important. As all active safeguard measures, intrusion detection technology remedies the shortcoming of traditional safeguard technology, and it becomes an important research field of network security. Now, there are some achievements and progress on intrusion detection are put into practice. And how to apply data mining arithmetic to intrusion detection efficiently is one of hot topics.Based on the basic concept and types of intrusion detection, this paper applies data mining (DM) technology into intrusion detection system to deal with the mass data. The membership grade function is optimized by the improved particle swarm algorithm. In the optimization process, the parameter combination of membership grade function is treated as particle. Then the best parameter combination can be searched in the particle iterative evolution. According to the prime parameters, the similarity between two association rule sets in the normal and abnormal state will be calculated. According to the small similarity, normal state and anomaly state could be differentiated in the most extent, the two states can be classified in the best way and the accuracy of anomaly detection will be improved greatly.