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数据挖掘在免疫入侵检测中的应用

Application of Data Mining in Immune Intrusion Detection
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摘要 论述了数据挖掘在免疫IDS系统中的应用,详细描述了关联规则和序列模式挖掘算法,在一定程度上弥补了阴性选择算法的不足,并提出了一个新的基于数据挖掘和人工免疫的入侵检测模型,克服了现有入侵检测模型的缺点。 This paper discusses the application of data mining in immune intrusion detection. To some extent, the association rules and sequence mining can make up the deficiency of negative selection algorithm. The paper also presents a new intrusion detection model based on data mining and artificial immune, which overcome the shortcoming of the current model.
出处 《孝感学院学报》 2005年第3期66-68,共3页 JOURNAL OF XIAOGAN UNIVERSITY
基金 国家自然科学基金(90204011) 湖北省科技攻关计划(2004AA01C001)
关键词 数据挖掘 人工免疫 入侵检测 阴性选择算法 data mining artificial immune intrusion detection negative selection algorithm
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参考文献4

  • 1Forrest. Self-Noself Discrimination in a computer[M]. Proceedings of 1994 IEEE syrmposium on Research in Security and Privacy, Los Alamos, CA:IEEE Computer Society Press, 1994.
  • 2Kim J,Bentley P. Negative selection within an artificial immune system for ertwork intrusion detection[C]. Proceedings of Genetic and Evolutionary Computation Conference. San Francisco: Morgan Kaufmann Publishers, 1999.
  • 3R Agrawal, et al. Mining Assosiation Rules Between Sets of ite ms in Large Database[C]. Proc. of the ACM SIGMOD Conference on Manage of Data,Washington D. C, 1993. 207-216.
  • 4R Agrawal,R Srikant. Mining Sequential Patterns:Generalizations and Performance Improvement[C].Proceeding of the Fifth Intl Conference on Extending Database Technology(EDBT),1996 3 17.

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