The Application Of Data Mining In Intrusion Detection

In today’s global information technology, information networks have been related to national defense, military, political, economic, cultural, educational and many other areas, and also play a crucial role in social production and life. The complexity of computer network, the resulting network security problem is increasingly serious. Intrusion detection system as one of the important network security protection technology, has become an important research object in the field of network security.With the continuous development of network, the invasion grew more complicated and diversified, the intrusion detection technology demand is higher and higher. For less than the current intrusion detection technology, the paper data mining technology into intrusion detection, from the perspective of data processing techniques for intrusion detection research and improvement.Firstly, the concept of intrusion detection and related technologies and data mining techniques are described in detail, for the introduction of intrusion detection data mining provides a theoretical basis; then the focus of the analysis of the clustering algorithm K-means algorithm and associated analysis algorithm Apriori algorithm for K-means algorithm and Apriori algorithm shortcomings related improvements proposed by the experiment proves the feasibility of improvements. Finally, the Snort intrusion detection system is analyzed, the introduction of data mining algorithms based on Snort system, build a model of an intrusion detection system based on data mining, and the system for detailed analysis and discussion, K-means algorithm improved and Apriori algorithm is applied to them.Test results show that the data mining is introduced into intrusion detection not only improve the detection efficiency and makes the system has the ability to detect an unknown attack.