Research of the Intrusion Detection Model Based on Data Mining

摘要：

The paper presents a new intrusion detection model combining misuse detection and anomaly defection mode, and makes a research into the key technology of the model based on data mining theory. In the model, the association rules data mining algorithm is applied to establish abnormal behavior rule set for misuse detection to detect known intrusion rapidly. And the minimum dissimilarity clustering analysis algorithm is used to establish normal behavior rule set for anomaly detection to detect new unknown intrusion. Research of the model of intrusion detection based on data mining is made on KDD99 dataset. The experiment shows that the new model can improve true positives, decrease false positives and detect new intrusions.

关键词： intrusion detection model association rules clustering analysis misuse detection anomaly detection

作者: Mei Jiang Xindan Gan Chaofeng Wang Zhuo Wang

作者单位: School of Computer Engineering, Qingdao Technological University Qingdao, China Global InfoTech Co..Ltd. Beijing, China

会议类型: 国际会议

会议名称: 2010 International Conference on Information Security and Artificial Intelligence(2010年信息安全与人工智能国际会议 ISAI 2010)

会议地点: 成都

会议语种:英文

页码: 1461-1465

在线出版日期: 2010-12-17（万方平台首次上网日期，不代表论文的发表时间）