期刊文献+

基于优化Apriori算法的入侵检测系统模型设计 被引量:1

IDS Model Designing Based on Optimize Apriori Algorithm
下载PDF
导出
摘要 复合攻击是网络入侵的主要形式之一。如何检测复合攻击是当前入侵检测研究的一个重要方向,经过对复合攻击模式的大量研究,提出了一种基于自动调节的警报关联模型。为了提高入侵检测系统的效率,针对入侵检测系统的特点,将数据挖掘技术引入模型中。阐述了使用为关联规则提取所优化的Apriori算法,对日志文件进行特征分析与知识发掘的入侵检测系统模型的设计。 The multi-step attack is one of the primary forms of the current intrusions. How to detect these attacks is an important aspect of IDS(Intrusion Detection System) research. Through the study on patterns of the multi-step attack, a model of alert correlation which is based on self-regulate is designed. To improve eficiency of IDS,the paper applies data mining technology to IDS according to the characteristics of the system.It describes how to acquire the intrusion knowledge from the logs and detect the intrusion behaviors based on the improved Apriori algorithm.
作者 李阳 朱宗胜
机构地区 安阳工学院
出处 《计算机安全》 2009年第11期20-22,共3页 Network & Computer Security
关键词 入侵检测 警报关联 自动调节 APRIORI算法 Intrusion Detection alert correlation self-regulate Apriori algorithm
  • 相关文献

参考文献1

二级参考文献9

  • 1[1]Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD International Conference on Management of Date, Washington DC, 1993.207~216
  • 2[2]Agrawal R, Srikant R. Fast algorithm for mining association rules. In: Proceedings of the 20th International Conference on VLDB, Santiago, Chile, 1994. 487~499
  • 3[3]Han J, Kamber M. Data Mining: Concepts and Techniques. Beijing: Higher Education Press, 2001
  • 4[5]Agrawal R, Shafer J C. Parallel mining of association rules:Design, implementation, and experience. IBM Research Report RJ 10004,1996
  • 5[6]Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules. In: Proceedings of the 21th International Conference on VLDB, Zurich, Switzerland, 1995. 432~444
  • 6[7]Hah J, Jian P et al. Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD International Conference on Management of Data, Dallas, TX, 2000.1~12
  • 7[8]Cheung D W, Lee S D, Kao B. A general incremental technique for maintaining discovered association rules. In: Proceedings of databases systems for advanced applications, Melbourne, Australia, 1997. 185~194
  • 8[10]Han J, Jian P. Mining access patterns efficiently from web logs. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'00), Kyoto, Japan,2000. 396~407
  • 9[11]Agrawal R, Srikant R. Mining sequential pattern. In: Proceedings of the 11th International Conference on Data Engineering, Taipei, 1995. 3~14

共引文献79

同被引文献10

引证文献1

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部