期刊文献+

一种基于数理统计的入侵检测算法研究 被引量:2

The Study of an Intrusion Detection Arithmetic Model Based on Normal Distribution
下载PDF
导出
摘要 提出了入侵检测的基本理论和基本概念,对入侵检测技术基本原理进行了分析和探讨,设计了一个异常入侵检测系统基本结构,重点讨论了基于统计模型的异常入侵检测技术。根据概率论有关理论,构造了一个基于数理统计分析的异常入侵检测系统模型。 The paper puts forward the basic theory and concept of intrusion detection and discusses the essential principle of intrusion detection technology, especially discusses the technology based on statistic model. At the same time, a basic structure of abnormal intrusion detection system has been designed in the paper. According to the probability theory, a new abnormal intrusion detection system model based on normal distribution is also constructed. Comparing to others, the arithmetic model is simple implement, better operative and reliable. The arithmetic model is worth applying and popularizing.
作者 李康荣
出处 《微计算机信息》 2009年第12期94-95,共2页 Control & Automation
基金 基金申请人:李康荣 基金颁发部门:四川省教育厅计算机软件省级重点实验室(SCSL07003) 项目名称:基于Web应用安全的质量保证研究
关键词 数理统计 异常 检测技术 算法模型 mathematical statistics abnormal detection technology arithmetic model
  • 相关文献

参考文献6

  • 1Jai SUnder Balasubramaniyan,Jose Dmar Garcia-Fernandez, David Isacoff et al,An Arch itecture for Intrusion Detection using Autonomous Agents[R].COST Technical Report 98/05.
  • 2Autonomous Agents[R].COST Technical Report 98/05. [2]Aurobindo Sundaram.An Introduction to Intrusion Detection. ACM Crossoads 2.4 1996 ,http://www.acm.org/crossroads/trds2-4/ intrus.htm.
  • 3KUMAR S.Classitication and Detection of Computer Intrusion [D].Dissertation,Purdue University,1995.
  • 4左冰,闫怀志,胡昌振.入侵检测技术探讨[J].探测与控制学报,2004,26(1):61-64. 被引量:2
  • 5卿斯汉,蒋建春,马恒太,文伟平,刘雪飞.入侵检测技术研究综述[J].通信学报,2004,25(7):19-29. 被引量:234
  • 6叶苗,王勇,麦范金.基于改进的层叠SVM模型的入侵检测技术[J].微计算机信息,2008,24(3):78-79. 被引量:3

二级参考文献56

  • 1叶苗.基于数据融合中Dempster—Shafer证据理论的入侵检测技术[J].广西大学梧州分校学报,2005,15(3):98-99. 被引量:1
  • 2[10]KDD Cup 1999 Data:http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
  • 3[11]王帅伟《支持向量机在入侵检测中的应用研究》[D]桂林桂林电子工业学院,2005
  • 4[5]J.C.Platt.Fast training of support vector machines using sequential minimal optimization,Advances in Kernel Methods[M].MIT Press 1998.
  • 5[6]B.L Lu,K.A.Wang,Y.M.Wen.Comparison of parallel and cascade methods for training support vector machines on largescale problems[C]//.Proceedings of the 3rd International Conference:.Aug.,2004:.3056-3061,.
  • 6[7]Jian-pei Zhang,Zhong-Wei Li,Jing Yang.A Parallel SVM Training Algorithm on Large-scale Classification Problems[//C],Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,Guangzhou,18-21 August 2005:1637-1641
  • 7[8]H.-P.Graf,E.Cosatto,L.Bottou,I.Dourdanovic,and V.Vapnik.Parallel support vector machines:The Cascade SVM[M],Advances in Neural Information Processing Systems,2005,17..
  • 8[9]Bao-Liang Lu,Kai-an Wang,Yi-Wen,J.Comparison Of Parallel and Cascade Methods for Trainning Support Vector Machines on Large-Scale Problems[//C]..Proceedqs of the Third International Conference on Machine Learning and Cybernetics,August 200
  • 9LEE W,STOLFO S,MOK K. A data mining framework for adaptive intrusion detection[EB/OL]. http://www.cs.columbia.edu/~sal/ hpapers/framework.ps.gz.
  • 10LEE W, STOLFO S J, MOK K. Algorithms for mining system audit data[EB/OL]. http://citeseer.ist.psu.edu/lee99algorithms.html. 1999.

共引文献235

同被引文献15

  • 1鲜继清,郎风华.基于模糊聚类理论的入侵检测数据分析[J].重庆大学学报(自然科学版),2005,28(7):74-77. 被引量:4
  • 2彭宏.基于粗糙集理论的入侵检测方法研究[J].电子科技大学学报,2006,35(1):108-110. 被引量:13
  • 3张钹 张铃.商空间理论与粒度计算.计算机科学,2003,30(5):1-3.
  • 4ZADEH L A. Fuzzy logic-computing with words [J]. IEEE Transactions on Fuzzy Systems, 1996, 4: 103-111.
  • 5ZADEH L A. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic [J]. Fuzzy Sets and Systems, 1997, 90: 111-121.
  • 6YAO Y Y. Granular compuying: basic issues and possible solutions [C]// Proc. of the 5th Joint Conf. on Information Sciences. Atlantic City, NJ: [s. n.], 2000: 186-189.
  • 7YAO Y Y, ZHONG N. Potential applications of granular computing in knowledge discovery and data mining[C]// Proc. of World Multiconference on Systemics, Cybernetics and Informatics [S.l.]:WMSCI, 1999 :573-580.
  • 8ZADEH L. Fuzzy sets and information granularity [C]// Advances in Fuzzy Set Theory and Applications, North Holland. Amsterdam: [s. n. ], 1979.3-18.
  • 9PEDRYCZ W. Granular computing: an emerging paradigm [M]. [S. l. ]: Physica-Verlag, 2001.
  • 10PAWLAK Z. Rough classification [J]. Int. Man-Machine Studies, 1984, 20: 469-483.

引证文献2

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