Network Intrusion Detection System in a Light Bulb

TL;DR

This paper presents a low-power, machine learning-based network intrusion detection system specifically designed for IoT edge devices, demonstrated on a smart light bulb, showing improved detection and efficiency over existing solutions.

Contribution

The paper introduces a novel, efficient ML-based NIDS tailored for low-power IoT devices, with practical implementation on a common low-power chipset like ESP8266.

Findings

Higher detection performance than existing edge-based NIDS

Faster and smaller model suitable for low-power IoT devices

Successfully demonstrated on a smart light bulb

Abstract

Internet of Things (IoT) devices are progressively being utilised in a variety of edge applications to monitor and control home and industry infrastructure. Due to the limited compute and energy resources, active security protections are usually minimal in many IoT devices. This has created a critical security challenge that has attracted researchers' attention in the field of network security. Despite a large number of proposed Network Intrusion Detection Systems (NIDSs), there is limited research into practical IoT implementations, and to the best of our knowledge, no edge-based NIDS has been demonstrated to operate on common low-power chipsets found in the majority of IoT devices, such as the ESP8266. This research aims to address this gap by pushing the boundaries on low-power Machine Learning (ML) based NIDSs. We propose and develop an efficient and low-power ML-based NIDS, and demonstrate its applicability for IoT edge applications by running it on a typical smart light bulb. We also evaluate our system against other proposed edge-based NIDSs and show that our model has a higher detection performance, and is significantly faster and smaller, and therefore more applicable to a wider range of IoT edge devices.