# IoTDevID: A Behavior-Based Device Identification Method for the IoT

arXiv: https://arxiv.org/abs/2102.08866 · HTML: https://mlsanity.com/paper/2102.08866/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [Machine Learning Methods for Attack Detection in the Smart Grid](https://mlsanity.com/paper/1503.06468/) (2015)
- [IoTSense: Behavioral Fingerprinting of IoT Devices](https://mlsanity.com/paper/1804.03852/) (2018)

## Built on by (most cited first, 4 of 4)

- [IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection](https://mlsanity.com/paper/2401.01343/) (2023)
- [Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey](https://mlsanity.com/paper/2403.16149/) (2024)
- [GeMID: Generalizable Models for IoT Device Identification](https://mlsanity.com/paper/2411.14441/) (2024)
- [IoT Device Identification with Machine Learning: Common Pitfalls and Best Practices](https://mlsanity.com/paper/2601.20548/) (2026)

## Abstract (arXiv)

Device identification is one way to secure a network of IoT devices, whereby devices identified as suspicious can subsequently be isolated from a network. In this study, we present a machine learning-based method, IoTDevID, that recognizes devices through characteristics of their network packets. As a result of using a rigorous feature analysis and selection process, our study offers a generalizable and realistic approach to modelling device behavior, achieving high predictive accuracy across two public datasets. The model's underlying feature set is shown to be more predictive than existing feature sets used for device identification, and is shown to generalize to data unseen during the feature selection process. Unlike most existing approaches to IoT device identification, IoTDevID is able to detect devices using non-IP and low-energy protocols.