@inproceedings{640f5e796d8846e79a76895dfb584da6,
title = "IoT or NoT: Identifying IoT Devices in a Short Time Scale",
abstract = "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.",
author = "Anat Bremler-Barr and Haim Levy and Zohar Yakhini",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE/IFIP Network Operations and Management Symposium, NOMS 2020 ; Conference date: 20-04-2020 Through 24-04-2020",
year = "2020",
month = apr,
doi = "https://doi.org/10.1109/NOMS47738.2020.9110451",
language = "الإنجليزيّة",
series = "Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020",
address = "الولايات المتّحدة",
}