Abstract
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.
Highlights
Nowadays, the Internet is dramatically growing with the proliferation of a wide variety of network-connected devices everywhere
The edge computing paradigm is emerging and attracting increasing attentions, which enables the use of networked resources from remote cloud datacenters to the edge closer to the data source [4]
The performance of the proposed Edge intrusion detection system (IDS) as well as the gene immune detection algorithm (GIDA) will be evaluated in detail, with comparative simulation experiments
Summary
The Internet is dramatically growing with the proliferation of a wide variety of network-connected devices everywhere. The first one is the monitor layer (e.g., Cloud IDS), the second one is the control layer (e.g., Edge IDS), and the third one is the device layer (e.g., host firewall) It has dynamic detection and adaptive functions because of the use of a distributed structure. Edge IDS can adaptively improve its security ability by acquiring security knowledge from Cloud IDS with continuous convergence of fragmented security data scattered throughout the network (3) An algorithm named GIDA is proposed It proposes the way of the remaining gene, niching strategy, and abstracting vaccine, so as to deal with dynamic data efficiently and own the function of self-learning.
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