SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Photonics

I-Shyan Hwang¹  Elaiyasuriyan Ganesan¹  Andrew Tanny Liem²  Mohammad Syuhaimi Ab-Rahman³ 

[1] Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, Taiwan;Department of Computer Science, Universitas Klabat Manado, North Sulawesi 95371, Indonesia;Department of Electrical, Electronics, and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia;

关键词: SDN-FiWi-IoT;    QoS-mapping;    network traffic classification;    machine learning;   

DOI  :  10.3390/photonics8060201

来源: DOAJ

【 摘 要 】

Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.

【 授权许可】

Unknown