A Novel Rule-based Intrusion Detection System Using Data Mining

摘要：

Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. 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. Association rules and sequence rules are the main technique of data mining for intrusion detection. Considering the classical Apriori algorithm with bottleneck of frequent itemsets mining, we propose a Length-Decreasing Support to detect intrusion based on data mining, which is an improved Apriori algorithm. Experiment results indicate that the proposed method is efficient.

关键词： Intrusion Detection Rule-based Length-Decreasing Support Association Rules Data Mining

作者: Lei Li De-Zhang Yang Fang-Cheng Shen

作者单位: School of Automation Nanjing University of Posts and Telecommunications Nanjing China

会议类型: 国际会议

会议名称: 2010 3rd IEEE International Conference on Computer Science and Information Technology(第三届IEEE计算机科学与信息技术国际会议 ICCSIT 2010)

会议地点: 成都

会议语种:英文

页码: 169-172

在线出版日期: 2010-07-07（万方平台首次上网日期，不代表论文的发表时间）