An Edge IDS Based on Biological Immune Principles for Dynamic Threat Detection

Abstract EN

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.