期刊文献+

新型的基于移动Agent自适应入侵检测系统研究 被引量:4

STUDY ON NOVEL MOBILE AGENT-BASED ADAPTIVE INTRUSION DETECTION SYSTEM
下载PDF
导出
摘要 本文提出了一种基于移动Agent自适应入侵检测系统 ,分析了各组成模块结构、功能特点以及模块之间的协作关系 ,并阐述了该系统设计中的信息表示及所采用的通信协议。该系统具有良好的扩展能力、自学习功能 ,并能及时处理微观的入侵行为 ,能够实现分布式的入侵检测。同时 ,该系统利用移动Agent特性 ,采用CIDF通信机制 ,以实现移动Agent之间的通信 。 This paper presents a mobile agent-based adaptive intrusion detection system(IDS).The structure,specific functions of these components and collaborations among them are described in details.General intrusion detection object based information expression format and relevant communication protocol applied in the system design are given.The system has not only such features as easy to be extended and distributed intrusion detection but also self-learning,high accuracy and low false alarm rate.
出处 《计算机应用与软件》 CSCD 北大核心 2004年第11期105-107,共3页 Computer Applications and Software
基金 江苏省高校自然科学基金项目 (编号 :0 3KJD52 0 1 0 6)
  • 相关文献

参考文献3

  • 1Stephen Northcutt: Network Intrusion Detection: An Analyst's handbook, Macmillan Techical Publishing,2000. 被引量:1
  • 2Honavar, V. Artificial Intelligence for Distributed Information Networks[ C](AiDIN′99) Workshop held during the 1999 National Conference on Artificial Intelligence (AAAI 99), Orlando,Florida. July 1999. 被引量:1
  • 3Andrew Honig, Andrew Howard,Eleazar Eskin. Adaptive Model Generation:An Architecture for Deployment of Data Mining-based Intrusion Detection Systems. Applications of Data Mining in Computer Security. Kluwer 2002. 被引量:1

同被引文献12

  • 1HAN J, KAMBER M. Data Mining: Concepts and Techniques[M]. Beijing: High Education Press, 2001. 被引量:1
  • 2WILSON R, MARTINEZ T. Improved Heterogeneous Distance Functions[J]. Journal of Artificial Intelligence Research, 1997, 6:1-34. 被引量:1
  • 3MANISH MEHTA, RAKESH AGRAWAL, JORMA RISSANEN. SLIQ: A Fast Scalable Classifier for Data Mining[Z]. Proceedings of the 5th International Conference on Extending Database Technology. Avigonon, 1996,18-32. 被引量:1
  • 4ANDREW HONIG, ANDREW HOWARD, ELEAZAR ESKIN. Adaptive Model Generation: an Architecture for Deployment of Data Mining-based Intrusion Detection Systems[A]. Data Mining for Security Applications[C]. [s.l.]: Kluwer Press, 2002. 被引量:1
  • 5Hatonen, K. et al. A Computer Host- Based User Anomaly Detection System Using the Self - Organizing Map [ C ]. International Joint Conference of Neural Networks, 2000. 被引量:1
  • 6Zanero S. et al. Unsupervised Learning Techniques for an Intrusion Detection System [ J]. AEM Symposium on Applied Computing, 2004, (2) :412 - 419. 被引量:1
  • 7Stephen Northcutt. Network Intrusion Detection:An Analystps handbook [ M ]. Itdly: Mac2 millan Technical Publishing, 2000. 被引量:1
  • 8Honavar, V. Artificial Intelligence for Distributed Information Networks[ C]. (AiDIN'99)Workshop held during the 1999 National Conference on Artificial Intelligence (AAAI 99), Orlando: 1999. 被引量:1
  • 9诸葛建伟.蜜罐及蜜网技术介绍[M].北京:北京大学计算机技术研究所,2001. 被引量:1
  • 10俞国燕,郑时雄,刘桂雄,黄平.复杂工程问题全局优化算法研究[J].华南理工大学学报(自然科学版),2000,28(8):104-110. 被引量:14

引证文献4

二级引证文献2

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