计算机应用 ›› 2013, Vol. 33 ›› Issue (03): 723-726.DOI: 10.3724/SP.J.1087.2013.00723

• 信息安全 • 上一篇    下一篇

基于Monte Carlo估计的免疫检测器分布优化算法

刘海龙1,2,张凤斌1*,席亮1   

  1. 1.哈尔滨理工大学 计算机科学与技术学院,哈尔滨 150080;
    2.哈尔滨师范大学 计算机科学与技术学院,哈尔滨 150025
  • 收稿日期:2012-09-18 修回日期:2012-10-28 出版日期:2013-03-01 发布日期:2013-03-01
  • 通讯作者: 席亮
  • 作者简介:刘海龙(1976-),男,黑龙江佳木斯人,博士研究生,主要研究方向:网络与信息安全; 张凤斌(1965-),男,黑龙江哈尔滨人,教授,博士生导师,博士,主要研究方向:网络与信息安全; 席亮(1983-),男,河北邢台人,讲师,博士,主要研究方向:网络与信息安全。
  • 基金资助:

    国家自然科学基金资助项目(60671049, 61172168)。

Immune detector distribution optimization algorithm with Monte Carlo estimation

LIU Hailong1,2, ZHANG Fengbin1*, XI Liang1   

  1. 1. College of Computer Science and Technology, Harbin University of Science and Technology, Harbin Heilongjiang 150080, China;
    2. College of Computer Science and Technology, Harbin Normal University, Harbin Heilongjiang 150025, China
  • Received:2012-09-18 Revised:2012-10-28 Online:2013-03-01 Published:2013-03-01
  • Contact: Liang XI
  • Supported by:

    ;the National Natural Science Foundation of China

摘要: 针对免疫实值检测器的黑洞和边界入侵问题,分析规模对检测性能的影响,提出一种基于Monte Carlo估计的检测器分布优化算法,以Monte Carlo方法估计检测器对非自体空间的覆盖效果作为算法结束的条件,通过优秀子代替代不合时宜的父代来完成检测器的分布优化处理。经实验测试表明,该算法不仅可以有效地降低黑洞,而且能够以更少的检测器更精确地覆盖非自体空间,从而提升检测器的检测性能。

关键词: 入侵检测, 免疫检测器, 分布优化, 否定选择算法, Monte Carlo估计

Abstract: In order to avoid lots of holes among mature immune detectors and deal with the problem of boundary invasion in intrusion detection, analyzing the relationship between number of detectors and detection performance, a detector distribution optimization algorithm with Monte Carlo estimation was proposed: evaluating the coverage of detectors by the Monte Carlo method, and updating the detector set by the offspring to improve detectors' distribution. The experimental tests demonstrate that the algorithm can not only decrease the holes but also achieve a more precise coverage of the nonself space with fewer detectors, and increase the detector's detection performance.

Key words: intrusion detection, immune detector, distribution optimization, negative selection algorithm, Monte Carlo estimation

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