Intrusion Detection Based On Clustering Algorithm

With the development of the technology and size of internet, more and more risk and possibility appeared on internet intrusion. The internet security has become an important issue of the whole world. Nowadays, what important is how to find out the new intrusion action fast and efficiency, and it is also important for the system and resource of the internet security. Intrusion detection is a new technique of security, which appeared after the traditional security measures like firewall, data encryption and so on. It is different from the traditional security measures, it is not passiveness but initiative. It is complementarity and technical improvement, too. By far, intrusion detection has been applying to the misuse recognition and the responses of computers and internet resource.Based on the research background stated above, this dissertation is to intend to develop research on network intrusion detection based on clustering method. In order to enhance the effectiveness for unknown intrusions, some network detection algorithms mainly using clustering analysis are proposed which are measured by the detection rate and the false positive rate, and are provided with computer simulations. This paper narrated the development of intrusion detection and introduced the structure of intrusion detection, after that it analyzed the advantages and disadvantages of traditional clustering algorithm that applying to intrusion detection, and then used a new clustering algorithm through the pre-disposal of KDD CUP 1999 datasets, such as continuous data, reduce dimension etc, and then applied to the algorithm The experiment proved it useful.