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

入侵检测与聚类分析 被引量：6

Intrusion Detection And Clustering Analysis
在线阅读 下载PDF
导出
摘要 传统的入侵检测方法在面对网络结构升级和未知攻击时 ,缺乏必要的扩展性和自适应能力 ,而基于机器学习的检测算法首先需要训练数据集进行训练 ,然后建立检测模型并通过测试数据集中入侵行为的检测结果来验证 ,此类方法由于获取类标识数据的困难性及其信息表达的局限性 ,降低了对未知攻击的检则能力。本文提出利用遗传聚类进行入侵检测算法IDUGC(IntrusionDetectionUsingGeneticClustering)。实验结果表明 ,此算法在未知入侵检测方面是可行的、有效的 。 Traditional intrusion detection methods lack extensibility and adaptability in the face of upgraded network architectures and unknown attacks. Meanwhile, detection algorithms based on machine learning need train data sets to train firstly, then the detection module is set up and validated by the detection result to intrusions in test data sets. Because of difficulties in obtaining labeled data and limitations to the knowledge expression to labeled data sets, the ability to detect unknown attacks is finally degraded. In this paper, Intrusion Detection Using Genetic Clustering (IDUGC) is proposed. By means of simulated experiments, this algorithm is proved feasible, efficient and extensible for unknown intrusion detection.
作者 刘勇国 彭军 张伟 李学明 廖晓峰 吴中福
机构地区 重庆大学计算科学与工程学院 重庆工业高等专科学校计算机系 重庆教育学院计算机与现代教育技术系
出处 《重庆工业高等专科学校学报》 2003年第1期4-8,共5页 Journal of Chongqing Polytechnic College
关键词 入侵检 聚类 遗传算法 intrusion detection clustering genetic algorithms
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献19

  • 1[1]Heady, R., Luger, G., Maccabe, A., et al. The architecture of a network level intrusion detection system[R]. Technical Report CS90-20, New Mexico: University of New Mexico, 1990.
  • 2[2]Allen, J., Christie, A., Fithen, W., et al. State of the Practice of Intrusion Detection Technologies[R]. Technical Report, CMU/SEI-99-TR-028, Pittsburgh: Carnegie Mellon University, 1999.
  • 3[3]Denning, D. E. An Intrusion-Detection Model[J]. IEEE Transactions on Software Engineering, 1987, 13(2):222-232.
  • 4[4]Denning, D. E., Neumann, P. G. Requirements and model for IDES-A real-time intrusion detection system[R]. Technical Report, CA: SRI International, 1985.
  • 5[5]Lunt, T. F. Detecting intruders in computer systems[EB/OL]. In: Proceedings of the 1993 Conference on Auditing and Computer Technology, 1993. http:∥www.ccert.edu.cn/documents/intrusion.pdf.
  • 6[6]Lee, W. A Data Mining framework for Constructing Features and Models for Intrusion Detection Systems[PhD Thesis]. Columbia University, USA, 1999.
  • 7[7]Eskin, E. Anomaly detection over noisy data using learned probability distributions[A]. In: Langley, P ed. Proceedings of the International Conference on Machine Learning[C]. CA: Morgan Kaufmann, 2000.
  • 8[8]Lee. W., Stolfo, S. J., Mok, K. Data mining in work flow environments: Experience in intrusion detection[A]. In: Chaudhuri, S ed. Proceedings of the 1999 Conference on Knowledge Discovery and Data Mining[C]. CA: ACM Press, 1999.
  • 9[9]Lee, W. Stolfo, S. J. Data mining approaches for intrusion detection. In: Rubin, A ed[A]. Proceedings of the 1998 USENIX Security Symposium. Texas: USENIX Association, 1998.
  • 10[10]Portnoy, L., Eskin, E., Stolfo, S. J. Intrusion detection with unlabeled data using clustering. In: Barbara, D ed. Proceedings of ACM CSS Workshop on Data Mining Applied to Security. Philadelphia: ACM Press, 2001.

同被引文献27

  • 1Portnoy L,Eskin E,Stolfo S J.Intrusion detection With unlabeled data using clustering[C].In:Barbara,Ded ed.Proceedings of ACM CSS Workshop on Data Mining Applied to Security,Philadelphia:ACM Press,2001.
  • 2.入侵实例[EB／0L].http://project.honeynet.org/.,.
  • 3Mukherjee B, Heberlein L T, Leivitt K N. Network Intrusion Detection[J]. IEEE Network, 1994,8(3):26-41.
  • 4MICHAEL J A B, LINOFF G.Data Mining Techniques: For Marketing, Sales,and Customer Support[M].New York:Wiley, 1997.
  • 5JOSEPH P B. Data Mining with Neural Networks[M].New York:McGraw-Hill,1996.
  • 6USAMA M F.Gregory Piatetsky-Shapiro,Padhraic Smyth,From Data Mining to Knowledge Discovery: An Overview[A].FAYYAD U M,PIATESTKY-SHAPIRO G,SMYTH P,et al.Advances in Knowledge Discovery and Data Mining[C].AAAI Press/The MIT,1996.
  • 7LEE Wenke,STOLFO Sal,MOK Kui. Mining Audit Data to Build Intrusion Detection Models[C].New York:Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining(KDD′98),1998.
  • 8LEE Wenke. STOLFO Sal,MOK Kui.A Data Mining Framework for Building Intrusion Detection Models[C]. Oakland CA:Proceedings of the 1999 IEEE Symposium on Secruity and Privacy,1999.
  • 9LEE Wenke,STOLFO Sal.Data Mining Approaches for Intrusion Detection[C]. San Antonio,TX:Proceedings of the 7th USENIX Security Symposium,1998.
  • 10PORTNOY L,ESKIN E, STOLFO S J. Intrusion Detection with Unlabeled Data Using Clustering[C]. Philadelphia:Proceedings of ACM CSS Workshop on Data Mining Applied to Security(DMSA-2001),ACM Press,2001.

引证文献6

二级引证文献19

重庆工业高等专科学校学报
2003年 第1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