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A Group-Based IoT devices Classification through Network Traffic Analysis based on Machine Learning Approach.

With the rapid growth of the Internet of Things (IoT), the deployment, management, and identification of IoT 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 IoT 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 IoT connected devices. Therefore, we design GBC?IoT, a group-based machine learning approach that enables to identify connected IoT devices through network traffic analysis. By leveraging well-known machine learning algorithms, GBC?IoT framework identifies and categorizes IoT devices into three classes with an overall accuracy e


Date de debut : 02 December 2020
Date de fin : 02 December 2020
Lieu : EAI AFRICOMM
Type : Conférence
Mise en ligne par : GUEYE Cheikh Ahmadou Bamba