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一种实时入侵检测专家系统的设计与实现 被引量:5

Design and Implementation of a Real-time Intrusion Detection Expert System
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摘要 该文在分析几种常见入侵检测方法的基础上,设计并实现了一种实时入侵检测专家系统,给出了详细的设计方案和实现方法。最后,分析了常见的入侵,并提供用于检测主机/端口扫描的规则集。实验结果表明,该系统能够快速有效地发现并报告多种入侵,能集成到网络安全产品中。 On the basis of analyzing of the general intrusion detection method,this paper designs and implements a real-time intrusion detection expert system.A detailed designing scheme and implementation method are provided.At last,this paper analyzes some general intrusion,and describes some rules about port scan.The experimental results reveal that the system can detect and report variety of intrusions,can be integrated into network security product.
出处 《计算机工程与应用》 CSCD 北大核心 2002年第9期9-10,43,共3页 Computer Engineering and Applications
基金 国家自然科学基金(编号:69983005) 国家教育部博士点基金(编号:RFDP1999048602)
关键词 入侵检测 专家系统 主机/端口扫描 网络安全 计算机网络 人工智能 设计 intrusion detection,Expert system,Host/port scan
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参考文献4

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共引文献13

同被引文献33

  • 1唐洪英,付国瑜.入侵检测的原理与方法[J].重庆工学院学报,2002,16(2):71-73. 被引量:25
  • 2危胜军,胡昌振,高秀峰.基于学习Petri网的网络入侵检测方法[J].兵工学报,2006,27(2):269-272. 被引量:5
  • 3Ye N,Li X Y,Chen Q,et al.Probabilistic techniques for intrusion detection based on computer audit data[J].IEEE Transaction on Systems, Man, and Cybemetics-Part A : Systems and Humans, 2001, 31(4):266-274.
  • 4Morteza A,Rasool J,Hamid R.SRT-UNNID:a practical solution to real-time network-based intrusion detection using unsupervised neural networks[J].Computers and Security, 2006,25 (6) : 459-468.
  • 5Tadeusz P,Axel T.Data mining and machine learning-towards reducing false positives in intrusion detection[J].Information Security Technical Report, 2005,10(3 ) : 169-183.
  • 6corrected.gz [DB/OL].http ://kdd.ics.uci.edu/databases/kddcup99/kd- dcup99.html.
  • 7Ye N, Li XY, Chen Q, et al. Probabilistic techniques for intrusion detection based on computer audit data [ J ]. IEEE Transaction on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2001, 31 (4): 266-274.
  • 8Morteza A, Rasool J, Hamid R. SRT-UNNID: a practical solution to real-time network-based intrusion detection using unsupervised neural networks [ J]. Computers and Security, 2006, 25 (6) : 459 - 468.
  • 9Tadeusz P, Axel T. Data mining and machine learningtowards reducing false positives in intrusion detection [ J ]. Information Security Technical Report, 2005, 10 ( 3 ) : 169 - 183.
  • 10corrected. gz[DB/OL], http: //kdd. ics. uci. edu/databases/kddcup99/kddcup99.html.

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