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基于Monte Carlo估计的免疫检测器分布优化算法 被引量：3

Immune detector distribution optimization algorithm with Monte Carlo estimation
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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。 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.
作者 刘海龙 张凤斌 席亮
机构地区 哈尔滨理工大学计算机科学与技术学院 哈尔滨师范大学计算机科学与技术学院
出处 《计算机应用》 CSCD 北大核心 2013年第3期723-726,共4页 journal of Computer Applications
基金 国家自然科学基金资助项目(60671049 61172168)
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 intrusion detection immune detector distribution optimization negative selection algorithm Monte Carloestimation
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
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共引文献13

同被引文献17

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计算机应用
2013年 第3期

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