# Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

arXiv: https://arxiv.org/abs/2101.10181 · HTML: https://mlsanity.com/paper/2101.10181/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 235 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 30 of 51)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Searching for A Robust Neural Architecture in Four GPU Hours](https://mlsanity.com/paper/1910.04465/) (2019)
- [Dynamic Network Surgery for Efficient DNNs](https://mlsanity.com/paper/1608.04493/) (2016)
- [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://mlsanity.com/paper/1904.12760/) (2019)
- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [Learning the Number of Neurons in Deep Networks](https://mlsanity.com/paper/1611.06321/) (2016)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [AdaNet: Adaptive Structural Learning of Artificial Neural Networks](https://mlsanity.com/paper/1607.01097/) (2016)
- [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)
- [Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments](https://mlsanity.com/paper/1909.11800/) (2019)
- [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018)
- [Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools](https://mlsanity.com/paper/1908.05557/) (2019)
- [Generative Adversarial Network for Wireless Signal Spoofing](https://mlsanity.com/paper/1905.01008/) (2019)
- [Evolutionary Neural AutoML for Deep Learning](https://mlsanity.com/paper/1902.06827/) (2019)
- [Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT](https://mlsanity.com/paper/1812.04872/) (2018)
- [DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms](https://mlsanity.com/paper/1904.07623/) (2019)
- [Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures](https://mlsanity.com/paper/1910.13312/) (2019)
- [Machine Learning Approach to RF Transmitter Identification](https://mlsanity.com/paper/1711.01559/) (2017)

## Built on by (most cited first, 9 of 9)

- [Class-Incremental Learning for Wireless Device Identification in IoT](https://mlsanity.com/paper/2105.06381/) (2021)
- [Zero-bias Deep Neural Network for Quickest RF Signal Surveillance](https://mlsanity.com/paper/2110.05797/) (2021)
- [NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks](https://mlsanity.com/paper/2303.06151/) (2023)
- [Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification](https://mlsanity.com/paper/2212.14677/) (2022)
- [What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale](https://mlsanity.com/paper/2510.13817/) (2025)
- [Efficient Adaptive Federated Optimization of Federated Learning for IoT](https://mlsanity.com/paper/2206.11448/) (2022)
- [A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN](https://mlsanity.com/paper/2208.01824/) (2022)
- [Leveraging Machine Learning for Accurate IoT Device Identification in Dynamic Wireless Contexts](https://mlsanity.com/paper/2405.17442/) (2024)
- [Smart Surveillance: Identifying IoT Device Behaviours using ML-Powered Traffic Analysis](https://mlsanity.com/paper/2512.13709/) (2025)

## Abstract (arXiv)

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to authenticate and verify legitimate devices' identities. However, cryptographic protocols are not available in many systems. Meanwhile, these methods are less effective when legitimate devices can be exploited or encryption keys are disclosed. Therefore, non-cryptographic IoT device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Non-cryptographic approaches require more effort and are not yet adequately investigated. In this paper, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators. We classify the IoT device identification and detection into four categories: device-specific pattern recognition, Deep Learning enabled device identification, unsupervised device identification, and abnormal device detection. Meanwhile, we discuss various ML-related enabling technologies for this purpose. These enabling technologies include learning algorithms, feature engineering on network traffic traces and wireless signals, continual learning, and abnormality detection.