Research of Data Mining Algorithm Based on the Intrusion Prevention System

Abstract

With the rapid development of network, the security problem of network becomes an issue which has been paid more and more attentions to. Among so many methods of intrusion prevention, data mining is a very effective one. The FP-growth algorithm is the most widely used algorithm for mining frequent item-sets, which is also an algorithm for mining association rules without candidate set. However, the FP-growth algorithm needs large memory when mining large database,and its running speed is slow. In order to overcome these problems, based on the FP-growth algorithm, this paper proposed an optimized algorithm. This paper compared the new algorithm with the previous one based on intrusion prevention model for campus network by experiments. Based on Experiments, we can draw the conclusion that, mining association rules by using the improved FP-growth algorithm can effectively detect the users’ behavior pattern, historical pattern and the current model to calculate the similarity of users, and provides the possibility to accurately judge the user behavior.