A NOVEL SIGNATURE SEARCHING FOR INTRUSION DETECTION SYSTEM USING DATA MINING

摘要：

Intrusion Detection System (IDS) has recently emerged as an important component for enhancing information system security. Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. In this paper, we propose a novel signature searching to detect intrusion based on data mining, which is an improved Apriori algorithm. We evaluate the capability of this new approach with the data from KDD 1999 data mining competition. Our experimental results demonstrate the potential of the proposed method.

关键词： Intrusion detection Data mining Association rule Apriori algorithm Frequent itemset Scenario

作者: YA-LI DING LEI LI HONG-QI LUO

作者单位: Pattern Recognition and Intelligence System, College of Automation, Nanjing University of Post and Telecommunications, Nanjing 210003, China

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 122-126

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）