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

异常入侵检测中数据挖掘技术RIPPER的应用 被引量：2

The Application of Data Mining Technology in Anomaly Detection
下载PDF
导出
摘要 介绍了入侵检测技术的分类以及数据挖掘技术在入侵检测中的应用,并阐述了构建的基于数据挖掘算法RIPPER的异常入侵检测系统的设计与实现. This paper introduces the categories of technology in anomaly detection. It also describes based on data mining algorithms, RIPPER. intrusion detection and the application of data mining the design and the implementation of the anomaly IDS
作者 赵玉明 滕少华 张巍 伍乃骐
机构地区 广东工业大学计算机学院
出处 《广东工业大学学报》 CAS 2005年第3期48-52,共5页 Journal of Guangdong University of Technology
基金 广东省自然科学基金团队项目(20003051) 广东省自然科学基金项目(041074117)
关键词 网络安全 系统调用 数据挖掘 RIPPER 入侵检测 network security system call data mining RIPPER intrusion detection
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8

  • 1Bace R. Intrusion Detection [M]. New York: Macmillan Technical Publishing, 2000.
  • 2Foreest S, Hofmeyr S A, Somayaji A. A sense of self for unix process[A]. Proceedings of 1996 IEEE Symposium on Computer Security and Privacy[C]. Oakland, California: IEEE Computer Society Press, 1996. 120-128.
  • 3Warrender C, Forrest S, Pearlmutter B. Detecting intrusion using system calls: alternative data modles[A]. Proceedings of 1999 IEEE Symposium on Computer Security and Privacy[C]. Oakland, California: IEEE Computer Society Press, 1999.133-145.
  • 4Sung-Bae Cho, Hyuk-Jang Park. Efficient anomaly detection by modeling privilege flows using hidden Markov model[J]. Computer & Science. 2003,22(1): 45-55.
  • 5Ghosh A K, Schwartzbard A, Shatz A M. Learning program behavior profiles for intrusion detection[A]. Proceedings of 1st USENIX Workshop on Intrusion Detection and Network Monitoring[C]. Santa Clara, CA: IEEE Computers Society Press, 1999. 67-74.
  • 6Wenke Lee, Salvatore J Stolfo. Learning patterns from unix process execution traces for intrusion detection[A]. In AAAI Workshop on AI Approaches to Fraud Detection and Risk Management[C], AAAI Press, 1997. 50-56.
  • 7William W, Cohen. Fast effective rule induction[A]. In Machine Learning: Proceeding of the Twelfth International Conference[C]. Lake Taho, California, Morgan Kaufmann.1995. 38-47.
  • 8Hofmeyr S A, Foreest S, Somayaji A. Intrusion detection using sequences of system Calls [J]. Journal of Computer Security, 1998,6:151-180.

同被引文献17

  • 1宋佳丽.采用分类挖掘模式提取网络入侵模型[J].网络安全技术与应用,2006(9):21-23. 被引量：1
  • 2李响,李庆波,徐怡庄,张广军,吴瑾光,杨丽敏,凌晓锋,周孝思,王健生.KNN方法在癌症中红外光谱检测中的应用[J].光谱学与光谱分析,2007,27(3):439-443. 被引量：12
  • 3Agrawal R, Shafer J C. Parallel mining of association rules[J]. IEEE Transaction on knowledge and Data Engineering, 1996,8 (6):962-969.
  • 4Zhang S. Nearest neighbor selection for iteratively KNN im- putation[J]. The Journal of Systems and Software, 2012, 85( 11 ) :2541-2552.
  • 5Cover T M, Hart P E. Nearest Neighbor Pattern Classifica- tion[J]. IEEE Transaction on Information Theory, 1967: 21-27.
  • 6Nikolai D, Klaus D B. The influence of deep convective motions on the variability of Z -Rrelations [ J ]. Atmospher- ic Research, 2001, 59(14):15-39.
  • 7Tsymbal A. Puuronen S. Bagging and boosting with dy- namic integration of classifiers, principles [ C ]. New York : Lecture notes in computer science, 2000 : 116-125.
  • 8Toh Koon Charlie Neo, Dan Ventura. A direct boosting al- gorithm for the k-nearest neighbor classifier via local war- ping of the distance metric [ J]. Pattern Recognition Let- ters, 2012,33 ( 1 ) : 102-92.
  • 9Kevin B, Collins Ray L I. Trusting emergence: some ex- periences of learning about integrated catchment science with the environment agency of england and wales [ J ]. Water Resources Management, 2010, 24(4) :669-688.
  • 10Tan P N, Michael Steinbach, Vipin Kumar. Introduction to data mining(Vet. 2) [M]. Beijing: Posts & Telecom Press, 2011.

引证文献2

二级引证文献10

广东工业大学学报
2005年 第3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