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计算机工程 ›› 2011, Vol. 37 ›› Issue (14): 131-133. doi: 10.3969/j.issn.1000-3428.2011.14.043

• 安全技术 • 上一篇    下一篇

基于猴群算法的入侵检测技术

张佳佳,张亚平,孙济洲   

  1. (天津大学计算机科学与技术学院,天津 300072)
  • 收稿日期:2011-01-06 出版日期:2011-07-20 发布日期:2011-07-20
  • 作者简介:张佳佳(1986-),男,硕士研究生,主研方向:信息安全;张亚平,副教授、博士;孙济洲,教授、博士
  • 基金资助:
    国家自然科学基金资助项目(60776807);天津市应用基础及前沿技术研究计划基金资助重点项目(09JCZDJC16800)

Intrusion Detection Technology Based on Monkey Algorithm

ZHANG Jia-jia, ZHANG Ya-ping, SUN Ji-zhou   

  1. (School of Computer Science and Techonology, Tianjin University, Tianjin 300072, China)
  • Received:2011-01-06 Online:2011-07-20 Published:2011-07-20

摘要: 针对入侵检测系统存在高漏报率的问题,提出一种基于猴群算法的入侵检测技术。利用猴群算法从网络审计数据KDD99数据集中生成一个分类的规则集合,采用支持度-置信度模型实现猴群算法的目标函数,以控制生成规则的质量,将动态生成的规则应用于基于规则的的入侵检测系统中。实验结果表明,基于猴群算法的入侵检测技术可改进生成规则的质量,提高入侵检测系统的检测率。

关键词: 入侵检测, 猴群算法, 支持度-置信度, 分类规则

Abstract: For the current Intrusion Detection System(IDS) has high false negative rate, this paper presents an intrusion detection technology based on Monkey Algorithm(MA). It uses the MA to derive a set of classification rules from network data, KDD99 data set, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are used to detect or classify network intrusions in a real-time environment. Experimental results show that the MA-based technology can improve the quality of generating rules, so that it can improve the performance of IDS.

Key words: intrusion detection, Monkey Algorithm(MA), support-confidence, classification rule

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