# IoT or NoT: Identifying IoT Devices in a ShortTime Scale

arXiv: https://arxiv.org/abs/1910.05647 · HTML: https://mlsanity.com/paper/1910.05647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

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

- [Device identification using optimized digital footprints](https://mlsanity.com/paper/2212.04354/) (2022)
- [Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey](https://mlsanity.com/paper/2403.16149/) (2024)

## Abstract (arXiv)

In recent years the number of IoT devices in home networks has increased dramatically. Whenever a new device connects to the network, it must be quickly managed and secured using the relevant security mechanism or QoS policy. Thus a key challenge is to distinguish between IoT and NoT devices in a matter of minutes. Unfortunately, there is no clear indication of whether a device in a network is an IoT. In this paper, we propose different classifiers that identify a device as IoT or non-IoT, in a short time scale, and with high accuracy. Our classifiers were constructed using machine learning techniques on a seen (training) dataset and were tested on an unseen (test) dataset. They successfully classified devices that were not in the seen dataset with accuracy above 95%. The first classifier is a logistic regression classifier based on traffic features. The second classifier is based on features we retrieve from DHCP packets. Finally, we present a unified classifier that leverages the advantages of the other two classifiers. We focus on the home-network environment, but our classifiers are also applicable to enterprise networks.