Research Article
A Group-Based IoT Devices Classification Through Network Traffic Analysis Based on Machine Learning Approach
307 downloads
@INPROCEEDINGS{10.1007/978-3-030-70572-5_12, author={Avewe Bassene and Bamba Gueye}, title={A Group-Based IoT Devices Classification Through Network Traffic Analysis Based on Machine Learning Approach}, proceedings={Towards new e-Infrastructure and e-Services for Developing Countries. 12th EAI International Conference, AFRICOMM 2020, Eb\'{e}ne City, Mauritius, December 2-4, 2020, Proceedings}, proceedings_a={AFRICOMM}, year={2021}, month={7}, keywords={Internet of Things Network traffic characteristics Machine learning algorithms}, doi={10.1007/978-3-030-70572-5_12} }- Avewe Bassene
Bamba Gueye
Year: 2021
A Group-Based IoT Devices Classification Through Network Traffic Analysis Based on Machine Learning Approach
AFRICOMM
Springer
DOI: 10.1007/978-3-030-70572-5_12
Abstract
With the rapid growth of the Internet of Things (), the deployment, management, and identification of devices that are connected to networks become a big concern. Consequently, they emerge as a prominent challenge either for mobile network operators who try to offer cost-effective services tailored to market, or for network administrators who aim to identify as well reduce costs processing and optimize traffic management of connected environments. In order to achieve high accuracy in terms of reliability, loss and response time, new devices real time discovery techniques based on traffic characteristics are mandatory in favor of the identification of connected devices.
Copyright © 2020–2024 ICST