Towards Remote Deployment for Intrusion Detection System to IoT Edge Devices

Abstract

Recently, low latency in data transmission has become one of the most critical requirements in developing the Internet of Things (IoT) applications. It triggers a novel network architecture, namely edge computing, that aims to move computing units close to data sources. This transformation emerges several security issues about designing and implementing security applications. An intrusion detection system (IDS), a well-designed system for detecting abnormal behaviors, needs to be transformed into modern system architectures. This article presents an edge-based architecture to quickly deploy a deep learning-based IDS to edge network devices regardless of the heterogeneity in hardware and deep learning model configurations. To demonstrate the effectiveness of our proposal, we also analyze various performance indicators of the architecture, deployment process, and deep-learning models.