A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

doi:10.1109/JAS.2018.7511192

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm
	Jinyin Chen
刊名	IEEE/CAA Journal of Automatica Sinica
	2020
卷号	7 期号:6 页码:1627-1637
关键词	Artificial immunity center optimized hybrid detect negative detector negative selection algorithm (NSA) radius adaptive
ISSN号	2329-9266
DOI	10.1109/JAS.2018.7511192
英文摘要	Negative selection algorithm (NSA) is one of the classic artificial immune algorithm widely used in anomaly detection. However, there are still unsolved shortcomings of NSA that limit its further applications. For example, the nonself-detector generation efficiency is low; a large number of nonself-detector is needed for precise detection; low detection rate with various application data sets. Aiming at those problems, a novel radius adaptive based on center-optimized hybrid detector generation algorithm (RACO-HDG) is put forward. To our best knowledge, radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity. RACO-HDG works efficiently in three phases. At first, a small number of self-detectors are generated, different from typical NSAs with a large number of self-sample are generated. Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible. Secondly, without any prior knowledge of the data sets or manual setting, the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism. In this way, the number of abnormal detectors is decreased sharply, while the coverage area of the nonself-detector is increased otherwise, leading to higher detection performances of RACO-HDG. Finally, hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected. Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate, lower false alarm rate and higher detection efficiency compared with other excellent algorithms.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/43064]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Jinyin Chen. A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(6):1627-1637.
APA	Jinyin Chen.(2020).A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm.IEEE/CAA Journal of Automatica Sinica,7(6),1627-1637.
MLA	Jinyin Chen."A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm".IEEE/CAA Journal of Automatica Sinica 7.6(2020):1627-1637.