# Network Intrusion Detection System in a Light Bulb

arXiv: https://arxiv.org/abs/2210.03254 · HTML: https://mlsanity.com/paper/2210.03254/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT](https://mlsanity.com/paper/2103.16329/) (2021)
- [Towards a Standard Feature Set for Network Intrusion Detection System Datasets](https://mlsanity.com/paper/2101.11315/) (2021)
- [Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System](https://mlsanity.com/paper/1704.02286/) (2017)
- [Exploring Edge TPU for Network Intrusion Detection in IoT](https://mlsanity.com/paper/2103.16295/) (2021)
- [Machine Learning Based IoT Intrusion Detection System: An MQTT Case Study (MQTT-IoT-IDS2020 Dataset)](https://mlsanity.com/paper/2006.15340/) (2020)

## Built on by (most cited first, 3 of 3)

- [FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems](https://mlsanity.com/paper/2304.14746/) (2023)
- [Towards Explainable Network Intrusion Detection using Large Language Models](https://mlsanity.com/paper/2408.04342/) (2024)
- [Design and implementation of intelligent packet filtering in IoT microcontroller-based devices](https://mlsanity.com/paper/2305.19214/) (2023)

## Abstract (arXiv)

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.