期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

网络入检测的快速规则匹配算法 被引量：2

A fast rule-matching algorithm in network intrusion detection
下载PDF
导出
摘要 在分析入侵检测系统的基础上,指出现有规则匹配算法的不足.提出一种新的规则匹配算法,该算法主要利用非精确匹配技术,缩小入侵分类的检测范围,达到快速匹配的目的.根据不同的安全性要求设置不同的门限值,该算法可用于预测适合不同门限值的可疑入侵行为. Based on the analysis of network intrusion detection systems, this paper points out the shortage of existing rule-matching algorithms, and then puts forward a new fast rule-matching algorithm. This algorithm is fast and effective because it uses the fuzzy matching method, and reduces the ranges of intrusion classes. In addition, such algorithm can be used to identify the suspicious intrusion behavior according to the different threshold that is set up based on various security levels.
作者 刘评 汤志国 于海峰
机构地区 北京系统工程研究所
出处 《海军工程大学学报》 CAS 2004年第5期71-73,共3页 Journal of Naval University of Engineering
关键词 入侵检测 网络安全 快速规则匹配 intrusion detection network security fast rule-matching
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献7

  • 1[1]Lee W, Stolfo S J, Kui W. Mining in a data-flow environment: experience in network intrusion detection [EB/OL]. http://www1. cs. columbia. edu/～wenke, 1999-08-01/2004-05-10.
  • 2[2]Lee W. A data mining framework for building intrusion detection models [EB/OL]. http://www1. cs. columbia. edu/～wenke, 1999-05-08/2004-05-12.
  • 3[3]Wang K, Stolfo S J. Anomalous payload-based network intrusion detection [EB/OL]. CERIAS Technical Report,1999-06-15/2004-05-10.
  • 4[4]Lee W. Toward cost-sensitive modeling for intrusion detection and response [EB/OL]. http://www1. cs. columbia. edu/～wenke, 2000-11-09/2004-05-13.
  • 5[5]Giacinto G, Roli F. Intrusion detection in computer networks by multiple classifier systems [EB/OL]. http://www. dice. unica. it/informatica/en/publications/ papers-prag/IDS-Conference-01. pdf, 2002-08-11/2004-05-12.
  • 6[6]Cohen W W. Fast effective rule induction [A]. In Machine Learning: the 12th International Conference [C]. Lake Taho, CA, Morgan Kaufmann,1995.
  • 7饶鲜,董春曦,杨绍全.基于支持向量机的入侵检测系统[J].软件学报,2003,14(4):798-803. 被引量：134

二级参考文献6

  • 1[1]Forrest S, Perrelason AS, Allen L, Cherukur R. Self_Nonself discrimination in a computer. In: Rushby J, Meadows C, eds. Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1994. 202～212.
  • 2[2]Ghosh AK, Michael C, Schatz M. A real-time intrusion detection system based on learning program behavior. In: Debar H, Wu SF, eds. Recent Advances in Intrusion Detection (RAID 2000). Toulouse: Spinger-Verlag, 2000. 93～109.
  • 3[3]Lee W, Stolfo SJ. A data mining framework for building intrusion detection model. In: Gong L, Reiter MK, eds. Proceedings of the 1999 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999. 120～132.
  • 4[4]Vapnik VN. The Nature of Statistical Learning Theory. New York: Spring-Verlag, 1995.
  • 5[5]Lee W, Dong X. Information-Theoretic measures for anomaly detection. In: Needham R, Abadi M, eds. Proceedings of the 2001 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 2001. 130～143.
  • 6[6]Warrender C, Forresr S, Pearlmutter B. Detecting intrusions using system calls: Alternative data models. In: Gong L, Reiter MK, eds. Proceedings of the 1999 IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999. 133～145.

共引文献133

同被引文献6

引证文献2

二级引证文献3

海军工程大学学报
2004年 第5期

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