Realization of Intrusion Detection System based on the Improved Data Mining Technology

On the basis of further analyzing the operational mechanism of the existing intrusion detection system model, in allusion to the existing problem the powerless, high false negative rate, low detection efficiency and the lack of the rule base automatic extension mechanism to unknown aggressive behavior for existing detection mechanisms, Combining the relevant knowledge of data mining technology, then to design one improved network intrusion detection system model based on data mining, combined misuse detection and anomaly detection. In the model, we select the K-means algorithm in clustering analysis and the Apriori algorithm in association rule mining and improve it. Applying the improved K-means algorithm to achieve normal behavior classes and data separation module, then utilizing the improved Apriori algorithm to achieve automatic extension of the rule base. Finally, by the experiment to verify the function of the two algorithms.