@inproceedings{a5eced33235b45578ec7e56f8fc6e594,
title = "Entropy-based IoT devices identification",
abstract = "The Internet of Things is now part of everyday life and there has been a wide range of novel IoT applications collecting cyber-physical data and providing information on the environment. As it is expected that the IoT traffic will count for a major part of the Internet traffic, it is essential to characterize the IoT traffic and to identify each device, and especially in the case of cyberattacks. In this paper, we present a new method to identify IoT devices based on traffic entropy. We compute the entropy values of traffic features and we rely on Machine Learning algorithms to classify the traffic. Our method succeeds in identifying devices under various network conditions with performances up to 94% in all cases. Our method is also robust to unpredictable network behavior with anomalies spreading into the network.",
keywords = "Anomaly, Entropy, Identification, IoT, Traffic Analysis",
author = "Hung Nguyen-An and Thomas Silverston and Taku Yamazaki and Takumi Miyoshi",
note = "Publisher Copyright: {\textcopyright} 2020 KICS. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 21st Asia-Pacific Network Operations and Management Symposium, APNOMS 2020 ; Conference date: 22-09-2020 Through 25-09-2020",
year = "2020",
month = sep,
doi = "10.23919/APNOMS50412.2020.9236963",
language = "English",
series = "APNOMS 2020 - 2020 21st Asia-Pacific Network Operations and Management Symposium: Towards Service and Networking Intelligence for Humanity",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "73--78",
booktitle = "APNOMS 2020 - 2020 21st Asia-Pacific Network Operations and Management Symposium",
}