Intrusion Detection System based on Data Mining

In this paper, Data Mining is introduced into the Intrusion Detection System, which overcomes the defects of traditional detection technology. The nuclear association rules algorithm applied to the intrusion detection matrix is optimized, which make it possible to reduce the Average-Case Time Complexity, improve the efficiency considerably, and make it easy to process magnanimity data. In this way, attacks will be detected promptly to achieve the goal of intrusion detection. Finally, the mining of normal connection rules in the knowledge base of intrusion detection matrix will be accomplished. The experiment indicates that the matrix is able to generate new rules after extracting features, and also proves the validity and the feasibility of the IDS.