期刊文献+

数据挖掘在入侵检测技术中的应用研究 被引量:1

Research on the application of data mining in intrusion detection
下载PDF
导出
摘要 针对入侵检测系统的特点,分析了数据挖掘在入侵检测技术中应用的研究现状,并利用数据挖掘技术在处理海量警报数据方面的优势,提出了一个入侵警报分析系统模型,通过对入侵检测系统产生的警报进行分析,减少了警报数量,提高了系统的检测效率和实用性。 In view of the intrusion detection system (IDS)'s characteristic, it analyzed the current research situation of the data mining (DM) application in intrusion detection (ID), and making use of superiority of DM in disposing massive alerts data, designed a model of intrusion alerts analysis system. It reduces the number of alerts, raises the detection efficiency and practicability by analyzing intrusion alerts.
出处 《齐齐哈尔大学学报(自然科学版)》 2009年第4期38-40,共3页 Journal of Qiqihar University(Natural Science Edition)
基金 黑龙江省教育厅科学技术研究项目(11531419)
关键词 入侵检测 数据挖掘 入侵警报 intrusion detection data mining intrusion alerts
  • 相关文献

参考文献5

  • 1Wenke Lee,Salvatore J.Stolfo.Data Mining Approaches for Intrusion Detection[C].Proceedings of the Seventh USENIX Security Symposium,Colorado,USA:1998,79-94.
  • 2Susan M.Bridges,Rayford B.Vaughn.Intrusion Detection Via Fuzzy Data Mining[C].Proceedings of the Twelfth Annual Canadian Information Technology Security Symposium,The Ottawa Congress Centrc,2000,109-122.
  • 3Stefanos Manganaris,Marvin Christensen,Dan Zerkle,Keith Hermiz.A Data Mining Analysis of RTID Alarms[J].Computer Networks.2000 (34):571-577.
  • 4宋广军,孙振龙.基于LRE算法的入侵检测警报分析系统的研究[J].计算机应用,2008,28(7):1776-1778. 被引量:3
  • 5MIT Lincoln Laboratory.DARPA intrusion detection evaluation data sets[EB/OL].[2009-3-5].http://www.ll.mit.edu/IST/ideval/data/data_index.html.

二级参考文献9

  • 1AGRAWAL R, IMIELIENSKI T, SWAMI A. Mining association rides between sets of items in large databases [ C]// Proceedings of the ACM SIGMOD Conference on Management of data. New York: ACM Press, 1993:207 -216.
  • 2AGRAWAL A, MANNILA H, SRIKANT R, et al. Fast discovery of association rules [ Z]. Cambridge: AAAI Press / MIT Press, 1996: 307 - 328.
  • 3HAN J, PEI H, YIN Y. Mining frequent patterns without candidate generation [ C]// 2000 ACM-SIGMOD. New York: ACM Press, 2000.
  • 4ZAKI M, PARTHASARATHY S, OGIHARA M, et al. New algorithms for fast discovery of association rules [ C] // Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Menlo Park: AAAI Press, 1997:283 -296.
  • 5BORGELT C. Efficient implementations of apfiori and eclat [ C]// Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations. Melbourne: IEEE Press, 2003.
  • 6WANG X M, BORGELT C, KRUSE R. Fuzzy frequent pattern discovery based on recursive elimination [ C]// ICMLA 2005. New York: IEEE Press, 2005:391 - 396.
  • 7BORGELT C. Keeping things simple: Finding frequent item sets by recursive elimination [ C]// OSDM 2005. New York: ACM Press, 2005: 66 - 70.
  • 8IBM Almaden Research Center. Synthetic data generation code for associations and sequential patterns [ EB/OL]. [ 2008 - 02 - 15] http://www. almaden. ibm. tom/software/quest/resources/index. shtml.
  • 9MIT Lincoln Laboratory. DARPA intrusion detection evaluation data sets[ EB/OL]. [ 2008 -02 - 15]. http://www. ll. mit. edu/IST/ ideval/data/data index. html.

共引文献2

同被引文献7

引证文献1

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