# Revisiting IoT Device Identification

arXiv: https://arxiv.org/abs/2107.07818 · HTML: https://mlsanity.com/paper/2107.07818/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications](https://mlsanity.com/paper/2102.08503/) (2021)
- [FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network](https://mlsanity.com/paper/1901.02358/) (2019)
- [IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT](https://mlsanity.com/paper/1611.04880/) (2016)
- [A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic](https://mlsanity.com/paper/1705.06805/) (2017)
- [A Haystack Full of Needles: Scalable Detection of IoT Devices in the Wild](https://mlsanity.com/paper/2009.01880/) (2020)
- [The Case for Retraining of ML Models for IoT Device Identification at the Edge](https://mlsanity.com/paper/2011.08605/) (2020)
- [PingPong: Packet-Level Signatures for Smart Home Device Events](https://mlsanity.com/paper/1907.11797/) (2019)
- [Image Classification on IoT Edge Devices: Profiling and Modeling](https://mlsanity.com/paper/1902.11119/) (2019)
- [An Analysis of Home IoT Network Traffic and Behaviour](https://mlsanity.com/paper/1803.05368/) (2018)
- [Network Service Dependencies in Commodity Internet-of-Things Devices](https://mlsanity.com/paper/1902.09647/) (2019)

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

- [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)
- [Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks](https://mlsanity.com/paper/2301.06695/) (2023)
- [GeMID: Generalizable Models for IoT Device Identification](https://mlsanity.com/paper/2411.14441/) (2024)
- [Unsupervised Baseline Clustering and Incremental Adaptation for IoT Device Traffic Profiling](https://mlsanity.com/paper/2602.24047/) (2026)
- [Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling](https://mlsanity.com/paper/2608.15465/) (2026)

## Abstract (arXiv)

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such, they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, while leveraging approaches previously proposed by other researchers. We compare the accuracy of four different previously proposed machine learning models (tree-based and neural network-based) for identifying IoT devices. We use packet trace data collected over a period of six months from a large IoT test-bed. We show that, while all models achieve high accuracy when evaluated on the same dataset as they were trained on, their accuracy degrades over time, when evaluated on data collected outside the training set. We show that on average the models' accuracy degrades after a couple of weeks by up to 40 percentage points (on average between 12 and 21 percentage points). We argue that, in order to keep the models' accuracy at a high level, these need to be continuously updated.