Edge computing solves such questions as the massive multisource data and resource consuming computing tasks in edge devices. Some new security problems especially the data security and privacy issues have been introduced into the edge computing scenario. Through analyzing the biological immune principles, a novel idea for the problem of intrusion detection in edge computing is provided. Specifically, an edge intrusion detection system (Edge IDS) with a distributed structure, which has the characteristics of an imprecise model, self-learning, and strong interactivity, is constructed in a systematic way inspired by the biological immune principles. Moreover, a newly proposed gene immune detection algorithm (GIDA) is designed. In order that Edge IDS can deal with the dynamic data problem efficiently, the key functional components such as the remaining gene, niching strategy, and extracting vaccine are embedded into the GIDA algorithm. Furthermore, extensive simulation experiments are conducted, and the results show that the proposed Edge IDS can be adapted to the domain of edge computing with comparative performance advantages.

中文翻译：

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基于生物免疫原理的边缘IDS用于动态威胁检测

边缘计算解决了诸如边缘设备中的大量多源数据和资源消耗计算任务之类的问题。边缘计算方案中引入了一些新的安全问题，尤其是数据安全和隐私问题。通过分析生物免疫原理，为边缘计算中的入侵检测问题提供了一种新颖的思路。具体而言，受生物免疫原理的启发，以系统的方式构建了具有结构不精确，自学性强，交互性强的分布式结构的边缘入侵检测系统（Edge IDS）。此外，设计了一种新提出的基因免疫检测算法（GIDA）。为了使Edge IDS可以有效地处理动态数据问题，GIDA算法中嵌入了关键的功能组件，例如剩余的基因，小生境策略和提取疫苗。此外，进行了广泛的仿真实验，结果表明，所提出的Edge IDS可以在具有相对性能优势的情况下适用于边缘计算领域。