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入侵检测系统中两种审计数据缩减技术的比较与分析 被引量：4

Comparison and Analysis of Two Audit Data Reduction Methods for Intrusion Detection System
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摘要 文中研究了构建IDS时重要输入特征的鉴别问题,在介绍一种基于效能等级的重要特征排序算法(PFRM)基础上,提出一种基于遗传算法的最小特征子集选取算法(FSSGA)。分析了两种算法的特点,并采用相同数据集对其进行实验比较。实验结果表明,与PFRM相比,FSSGA算法在特征减少和攻击检测方面具有更好的性能。 This paper addresses the issue of identifying important input features in building an intrusion detection system (IDS). An algorithm named PFRM was first introduced and then a new algorithm (FSSGA) based on GA is proposed. The characteristics of both algorithms were analyzed. The experimental result using the same dataset shows that the performance of FSSGA is much better than PFRM. 
作者 邹涛 孙宏伟 田新广 李学春
机构地区 国防科技大学电子科学与工程学院 北京首信股份有限公司网络技术研究所
出处 《计算机应用》 CSCD 北大核心 2003年第7期13-14,17,共3页 journal of Computer Applications
关键词 入侵检测系统 审计数据 遗传算法 支撑矢量机 神经网络 IDS audit data genetic algorithm support vector machine neural network
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献6

  • 1Mukkamala S, Sung AH, Abraham A. Identifying Key Variables for Intrusion Detection Using Soft Computing Paradigms[ A]. The IEEE International Conference on Fuzzy Systems FUZZ-IEEE'03[ C]. St Louis, MO, USA, 2003.
  • 2Mukkamala S, Janoski C, Sung AH. Comparison of Neural Networks and Support Vector Machines in Intrusion Detection[ A]. Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection[ C]. Baltimore, Maryland, USA, June 11 - 13, 2002.
  • 3Mukkamala S, Sung AH. Audit Data Reduction Using Neural Networks and Support Vector Machines[ A]. Digital Forensics Research Workshop ( DFRWS - 2002) [ C]. Syracuse University, Center for Systems Assurance, August 7 - 9, 2002.
  • 4Mukkamala S, Janoski G, Sung AH. Monitoring Information System Security[ A]. Proceedings of the 11th Annual Workshop on Information Technologies & Systems[ C]. New Orleans, Louisiana, USA,2001. 139- 144.
  • 5Mukkamala S, Janoski G, Sung AH. Intrusion Detection Using Neural Networks and Support Vector Machines[ A]. Proceedings of IEEE International Joint Conference on Neural Networks[ C]. Honolulu,Hawaii, USA, 2002. 1702 - 1707.
  • 6Vladimir VN. The Nature of Statistical Learning Theory[ M]. New York: Springer, 1995.

同被引文献31

  • 1郑军,胡铭曾,云晓春,张宏莉.基于SOFM和快速最近邻搜索的网络入侵检测系统与攻击分析[J].计算机研究与发展,2005,42(9):1578-1586. 被引量：3
  • 2边肇祺.模式识别[M].清华大学出版社,1999..
  • 3何倩.[D].桂林:桂林电子工业学院,2004—03.
  • 4Andrew H Sung.Identify important features for intrusion detection using support vector machines and neural networks[C].In:IEEE Proceedings of the 2003 Symposium on Application and the Internet, 2003.
  • 5Mukkamala Srinivas, Janoski Guadalupe, Sung Andrew.Intrusion detection using neural networks and support vector machines[C].In:Proceedings of the International Joint Conference on Neural Networks, vol.2,2002 : 1702-1707.
  • 6Botha Martin,von Solms Rossouw.Utilizing fuzzy logic and trend analysis for effective intrusion detection[J].Computers and Security, 2003 ; 22 (5) : 423-434.
  • 7Bala Jerzy,Baik Sung,Hadjarian Ali et al.Application of a distributed data mining approach to network intrusion detection[C].In:Proceedings of the Intematlonal Conference on Autonomous Agents, 2002:1419-1420.
  • 8Hossain Mahmood,Bridges Susan M,Vaughn Jr et al.Adaptive intrusion detection with data mining[C].In :Proc of the IEEE Int Conf on Systems,Man and Cybernetics,vol.4,2003:3097-3103.
  • 9Tim Bass.Intrusion Detection Systems Multisensor Data Fusion Creating Cyberspace Situational Awareness.http ://citeseer. nj.nec.com/ bass00intrusion.html 2001.
  • 10Wang Yong,Yang Huihua,Wang Xingyu.Distributed Intrusion Detection System Based on Data Fusion Method[C].In:The 5th World Congress on Intelligent Control and Automation(WCICA'04),Hangzhou, China, 2004.

引证文献4

二级引证文献10

计算机应用
2003年 第7期

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