# Privacy-Preserving Detection of IoT Devices Connected Behind a NAT in a Smart Home Setup

arXiv: https://arxiv.org/abs/1905.13430 · HTML: https://mlsanity.com/paper/1905.13430/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 2 of 2)

- [IoT Device Identification Based on Network Communication Analysis Using Deep Learning](https://mlsanity.com/paper/2303.12800/) (2023)
- [Locality Sensitive Hashing for Network Traffic Fingerprinting](https://mlsanity.com/paper/2402.08063/) (2024)

## Abstract (arXiv)

Today, telecommunication service providers (telcos) are exposed to cyber-attacks executed by compromised IoT devices connected to their customers' networks. Such attacks might have severe effects not only on the target of attacks but also on the telcos themselves. To mitigate those risks we propose a machine learning based method that can detect devices of specific vulnerable IoT models connected behind a domestic NAT, thereby identifying home networks that pose a risk to the telco's infrastructure and availability of services. As part of the effort to preserve the domestic customers' privacy, our method relies on NetFlow data solely, refraining from inspecting the payload. To promote future research in this domain we share our novel dataset, collected in our lab from numerous and various commercial IoT devices.