在网络安全中对网络异常数据进行检测十分必要.已有的V-detector否定选择算法在解决复杂网络数据问题时,随机生成检测器会消耗大量时间,不利于在线检测的快速实现.本文提出了一种基于Delaunay三角剖分的否定选择算法,在检测器的生成阶段使用三角剖分对检测器中心点进行定位,直接生成成熟检测器,并降低检测个数,从而降低检测器生成时的资源消耗.将本文算法与其他两种算法进行对比,实验结果表明,基于Delaunay三角剖分的否定选择算法在生成检测器时速度更快且生成个数更少,检测率也更高.
It is very necessary to detect network abnormal data in network security.When the existing V-detector negative selection algo-rithm solves the complex network data problem,the random generation of detector will consume a lot of time,which is not conducive to the rapid implementation of on-line detection.In this paper,a negative selection algorithm based on Delaunay triangulation is proposed.In the detector generation stage,triangulation is used to locate the center point of the detector,directly generate mature detectors,and reduce the number of detectors,so as to reduce the resource consumption of detector generation.Comparing the proposed algorithm with the other two algorithms,the experimental results show that the negative selection algorithm based on Delaunay triangulation has faster speed,a smaller number of detectors and higher detection rate.