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基于多传感器数据融合入侵检测模型 被引量:1

A Intrusion Detection Model based on Multi-sensor Data Fusion
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摘要 随着计算机和互联网应用技术的快速发展,网络的入侵检测技术越来越重要。本文是针对现有的网络入侵检测系统存在的问题,结合现有数据融合和数据挖掘算法技术的不断提高,给出了一种基于多传感器数据融合和数据挖掘的网络入侵检测模型。应用该模型能使阻拦入侵元素有依据可寻,同时能很好地减少网络入侵的危害程度,从而提高自身系统的免疫性。其功能在文中详细介绍。 With the fast development of the computing and networking technologies,the intrusion detection technology becomes more and more important.For the problems of existing intrusion detection systems and the development of existing data mining and fusion,this paper proposes an intrusion detection model based on the multi-sensor data fusion and data mining and details the functions of this model.This model could reduce the harm of network intrusion and thus emhance the self-immunity of the system.
作者 陈志航
出处 《通信技术》 2010年第11期112-114,共3页 Communications Technology
关键词 入侵检测 多传感器 数据融合 intrusion detection multisensor data fusion
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参考文献5

  • 1Denning D. An Intrusion Detection Model[ J]. IEEE Transactions on Software Engineering, 2004, SE - 13 (02) :222 - 232.
  • 2Snapp S. A System for Distributed Intrusion Detection[ C]. USA: IEEE, 2003 : 170 - 176.
  • 3Silverman R. Intrusion Detection Systems Sniff Out Digital Attack [ J ]. The Wall Street Journal, 2002(04) :36.
  • 4Bauer D, Koblentz M. An Expert System for Real - Time Network Intrusion Detection [ J]. Proceeding of the IEEE Computer Networking Symposium, 2005( 11 ) :17 - 13.
  • 5宋珊珊,李建华,张少俊.基于数据挖掘的因果关联知识库构建方法[J].信息安全与通信保密,2009,31(7):102-104. 被引量:3

二级参考文献3

  • 1Ning P, Cui Y, Reeves D. Constructing Attack Scenarios through Correlation of Intrusion Alerts[C]. In: Proc. of the 9th ACM Conference on Computer and Communications Security, Washington, DC, USA, 2002: 245-254.
  • 2Ning P, Cui Y, Reeves D, et al. Tools and Techniques for Analyzing Intrusion Alerts[J]. ACM Transactions on Information and System Security. 2004, 07(02) 273-318.
  • 3Mannila H, Toivonen H, Verkamo A. Discovery of frequent episodes in event sequences[J]. Data Mining and Knowledge Discovery. 1997, 01(03) 259-289.

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