# IoT Device Identification with Machine Learning: Common Pitfalls and Best Practices

arXiv: https://arxiv.org/abs/2601.20548 · HTML: https://mlsanity.com/paper/2601.20548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 28 Jan 2026.

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

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (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)
- [IoTDevID: A Behavior-Based Device Identification Method for the IoT](https://mlsanity.com/paper/2102.08866/) (2021)
- [Externally validating the IoTDevID device identification methodology using the CIC IoT 2022 Dataset](https://mlsanity.com/paper/2307.08679/) (2023)
- [Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms](https://mlsanity.com/paper/2010.08466/) (2020)

## Abstract (arXiv)

This paper critically examines the device identification process using machine learning, addressing common pitfalls in existing literature. We analyze the trade-offs between identification methods (unique vs. class based), data heterogeneity, feature extraction challenges, and evaluation metrics. By highlighting specific errors, such as improper data augmentation and misleading session identifiers, we provide a robust guideline for researchers to enhance the reproducibility and generalizability of IoT security models.