# IoT Device Identification Using Deep Learning

arXiv: https://arxiv.org/abs/2002.11686 · HTML: https://mlsanity.com/paper/2002.11686/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification](https://mlsanity.com/paper/2201.03281/) (2022)
- [SunBlock: Cloudless Protection for IoT Systems](https://mlsanity.com/paper/2401.14332/) (2024)
- [Rapid IoT Device Identification at the Edge](https://mlsanity.com/paper/2110.13941/) (2021)
- [IoT Device Identification Based on Network Communication Analysis Using Deep Learning](https://mlsanity.com/paper/2303.12800/) (2023)
- [GeMID: Generalizable Models for IoT Device Identification](https://mlsanity.com/paper/2411.14441/) (2024)

## Abstract (arXiv)

The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers due to the less secure nature of the devices. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization's network also increases the risk of attacks. In order to address this threat, organizations often implement security policies in which only the connection of white-listed IoT devices is permitted. To monitor adherence to such policies and protect their networks, organizations must be able to identify the IoT devices connected to their networks and, more specifically, to identify connected IoT devices that are not on the white-list (unknown devices). In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network. In contrast to previous work, our approach does not require that complex feature engineering be applied on the network traffic, since we represent the communication behavior of IoT devices using small images built from the IoT devices network traffic payloads. In our experiments, we trained a multiclass classifier on a publicly available dataset, successfully identifying 10 different IoT devices and the traffic of smartphones and computers, with over 99% accuracy. We also trained multiclass classifiers to detect unauthorized IoT devices connected to the network, achieving over 99% overall average detection accuracy.