The artificial immune system (AIS) is one of the important branches of artificial intelligence technology, and it is widely used in many fields. The detector set is the core knowledge set, and the AIS application effects are mainly determined by the generation, evolution, and detection of the detectors. Presently, the problem space (shape-space) of AIS mainly applied real-valued representation. But the real-valued detectors have some problems that have not been solved well, such as slow convergence speed of generation, holes in the nonself region, detector overlapping redundancy, dimension curse, etc., which lead to the unsatisfactory detection effects. Moreover, artificial immune anomaly detection is a dynamic adaptive model, needs to be evolved adaptively with the detection environments. Without better adaptive modeling, these problems mentioned before will get worse. In view of this, this article proposes a multisource immune detector adaptive model in neighborhood shape-space and applies it to anomaly detection: based on random, chaotic map and DNA genetic algorithm (DNA-GA), multisource neighborhood negative selection algorithm (MSNNSA), multisource neighborhood immune detector generation algorithm (MS-NIDGA), and neighborhood immune anomaly detection algorithm (NIADA) are proposed, so that the generation and detection of immune detectors can be improved efficiently; introducing immune adaptive and feedback mechanism, multisource neighborhood immune detector adaptive model (MS-NIDAM) is built, so that the detectors can be adaptively evolved in a more targeted search domain, and keep better distribution to the nonself region in real time, so as to solve various problems existing in the real-valued shape-space under dynamic environment mentioned before and improve the overall detection performances. The experimental results show that MS-NIDAM can improve the detector generation/evolution efficiency, keep the up-to-date understanding of the changing environment, so as to obtain better overall detection performances and stability than other comparative methods.

中文翻译：

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用于异常检测的多源邻域免疫检测器自适应模型

人工免疫系统（AIS）是人工智能技术的重要分支之一，广泛应用于多个领域。探测器集是核心知识集，AIS的应用效果主要由探测器的产生、演化和探测决定。目前，AIS的问题空间（shape-space）主要采用实值表示。但实值检测器存在的一些问题没有得到很好的解决，如生成收敛速度慢、非自体区域存在空洞、检测器重叠冗余、维数诅咒等，导致检测效果不理想。而且，人工免疫异常检测是一种动态自适应模型，需要随检测环境自适应地发展。如果没有更好的自适应建模，前面提到的这些问题会变得更糟。鉴于此，本文提出了一种邻域形状空间中的多源免疫检测器自适应模型并将其应用于异常检测：基于随机、混沌图和DNA遗传算法（DNA-GA）、多源邻域负选择算法（MSNNSA） ，提出了多源邻域免疫检测器生成算法（MS-NIDGA）和邻域免疫异常检测算法（NIADA），从而有效地改进了免疫检测器的生成和检测；引入免疫自适应和反馈机制，构建多源邻域免疫检测器自适应模型（MS-NIDAM），使检测器可以在更有针对性的搜索域中自适应进化，并实时更好地分布到非自身区域，从而解决前面提到的动态环境下实值形状空间存在的各种问题，提高整体检测性能。实验结果表明，MS-NIDAM 可以提高检测器的生成/进化效率，保持对不断变化的环境的最新理解，从而获得比其他比较方法更好的整体检测性能和稳定性。