基于检测器集层次聚类的否定选择算法  

Negative Selection Algorithm Based on Hierarchical Clustering of Detector Set

作　　者：王韫烨 孔珊 WANG Yunye;KONG Shan(College of Information Science and Technology,Zhengzhou Normal University,Zhengzhou 450044,China)

机构地区：[1]郑州师范学院信息科学与技术学院,郑州450044

出　　处：《计算机工程》2020年第6期303-307,共5页Computer Engineering

基　　金：国家自然科学基金(61572447);河南省科技攻关计划项目(162102310238)。

摘　　要：传统的否定选择过程需要将全部检测器与测试数据进行匹配以排除异常数据,该匹配过程需要花费大量时间,导致检测效率过低。为此,提出一种基于检测器集层次聚类的否定选择算法。对生成的检测器进行层次聚类,减少需要计算距离的检测器数量,不再将与检测器不匹配的数据标记为正常数据,而是基于该数据与自体集和检测器集距离的计算结果将其标记为正常数据或异常数据。实验结果表明,与V-detector算法和免疫实值否定选择算法相比,该算法的检测效率显著提高,误检率明显降低。The traditional negative selection process takes a long time to match all detectors with test data to eliminate abnormal data,resulting in low detection efficiency.Therefore,this paper proposes a negative selection algorithm based on hierarchical clustering of the detector set.The number of detectors that need to calculate the distance is reduced by hierarchical clustering of the generated detectors.The data that does not match the detector is no longer directly marked as normal data,but is marked based on the calculation results of the distance between the data and the self-set and the detector set.Experimental results show that compared with the V-detector algorithm and the real-valued negative selection algorithm of immunity,the proposed algorithm significantly improves the detection efficiency and reduces the false detection rate.

关 键 词：异常检测 检测器集 否定选择算法 层次聚类 检测效率 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP309[自动化与计算机技术—控制科学与工程]