Page 132 - 《软件学报》2021年第10期

软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
Journal of Software,2021,32(10):31043121 [doi: 10.13328/j.cnki.jos.006017] http://www.jos.org.cn
©中国科学院软件研究所版权所有. Tel: +86-10-62562563


邻域形态空间多源免疫检测器生成与检测 

席 亮, 姚之钰, 张凤斌

(哈尔滨理工大学 计算机科学与技术学院,黑龙江 哈尔滨 150080)
通讯作者: 席亮, E-mail: xiliang@hrbust.edu.cn

摘 要: 人工免疫系统(artificial immune system,简称 AIS)是人工智能技术的重要分支之一,被广泛应用于异常检
测、数据挖掘、机器学习等多个领域.检测器是其核心知识集,其生成、优化和检测操作决定了人工免疫的应用效
果.目前,人工免疫的问题空间以实值形态空间为主,但实值非自体空间“黑洞”、检测器生成速率慢、检测器高重叠
冗余、“维度灾难”等问题,使得人工免疫检测的效果不甚理想.鉴于此,使用邻域形态空间,并改进邻域否定选择算法
(neighborhood negative selection algorithm,简称 NNSA),引入混沌理论和遗传算法,提出了一种多源邻域否定选择算
法(multi-source-inspired NNSA,简称 MSNNSA),并基于此提出邻域形态空间多源免疫检测器生成与检测方法,改进
邻域形态空间下检测器的构造与生成机制,使其更具靶向性,并使获得的检测器具有更好的分布性,提高其生成效率
和整体的检测性能,解决以上实值形态空间下存在的问题.实验结果表明,该方法提高了检测器生成效率以及检测的
整体性能和稳定性.
关键词: 邻域形态空间;异常检测;否定选择;混沌映射;遗传算法
中图法分类号: TP18

中文引用格式: 席亮,姚之钰,张凤斌.邻域形态空间多源免疫检测器生成与检测.软件学报,2021,32(10):31043121. http://
www.jos.org.cn/1000-9825/6017.htm
英文引用格式: Xi L, Yao ZY, Zhang FB. Multi-source-inspired immune detector generation and detection in neighborhood
shape-space. Ruan Jian Xue Bao/Journal of Software, 2021,32(10):31043121 (in Chinese). http://www.jos.org.cn/1000-9825/
6017.htm

Multi-source-inspired Immune Detector Generation and Detection in Neighborhood Shape-
space

XI Liang, YAO Zhi-Yu, ZHANG Feng-Bin
(School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China)

Abstract: Artificial immune system (AIS) is one of the important branches of artificial intelligence technology, and it is widely used in
many fields such as anomaly detection, data mining, and machine learning. The detectors are its core knowledge set, and the application
effects are determined by the generation, optimization, and detection of the detectors. At present, the problem space of AIS mainly applied
real-valued shape-space. But the detectors in the real-valued shape-space have some problems that have not been solved, such as the holes
in the non-self-shape-space, slow speed of generation, detector overlapping redundancy, dimension curse, which lead to the unsatisfactory
detection effects. In view of this, based on the neighborhood shape-space, a new shape-space, and the improved neighborhood negative
selection algorithm, a multi-source-inspired neighborhood negative selection algorithm (MSNNSA) is proposed by introducing chaotic
map and genetic algorithm. And then, based on this algorithm, the multi-source-inspired immune detector generation and detection
methods in neighborhood shape-space are built to make the construction and generation more targeted, so that the generated detectors
have better distribution performance. Meanwhile, the method also improves the detectors’ generation efficiency and the detection

 基金项目: 国家自然科学基金(61172168); 黑龙江省自然科学基金(F2018019)
Foundation item: National Natural Science Foundation of China (61172168), Natural Science Foundation of Heilongjiang Province,
China (F2018019)
收稿时间: 2019-05-06; 修改时间: 2019-08-22, 2019-12-19; 采用时间: 2020-01-31