In this paper an intrusion detection system based on data mining is proposed, and its main idea is to apply data mining methods to learn rules that can capture normal and intrusion activities from pre- processed audit data that contain network connection information. Put forward a method to improve the Apriori algorithm, whose I/O is quite surprising when scanning the database. To improve the method is feasible the normal rules in the knowledge database in IDS are mined. And the experiment indicates that the model can produce new rules, which approve the validity and the feasibility of the IDS.

Research_on_Network_intrusion_detection_based_on_data_mining.pdf

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