Edge computing intelligence using robust feature selection for network traffic classification in internet-of-things

• Main Authors: Mohammed, Bushra, Hamdan, Mosab, Bassi, Joseph Stephen, Jamil, Haitham A., Khan, Suleman, Elhigazi, Abdallah, Rawat, Danda B., Ismail, Ismahani, Marsono, Muhammad Nadzir
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/91678/1/MuhammadNadzirMarsono2020_EdgeComputingIntelligenceUsingRobust.pdf; http://eprints.utm.my/id/eprint/91678/; http://dx.doi.org/10.1109/ACCESS.2020.3037492

Internet-of-Things (IoT) devices are massively interconnected, which generates a massive amount of network traffic. The concept of edge computing brings a new paradigm to monitor and manage network traffic at the network's edge. Network traffic classification is a critical task to monitor and identify Internet traffic. Recent traffic classification works suggested using statistical flow features to classify network traffic accurately using machine learning techniques. The selected classification features must be stable and can work across different spatial and temporal heterogeneity. This paper proposes a feature selection mechanism called Ensemble Weight Approach (EWA) for selecting significant features for Internet traffic classification based on multi-criterion ranking and selection mechanisms. Extensive simulations have been conducted using publicly-available traces from the University of Cambridge. The simulation results demonstrate that EWA is capable of identifying stable features subset for Internet traffic identification. EWA-selected features improve the mean accuracy up to 1.3% and reduce RMSE using fewer features than other feature selection methods. The smaller number of features directly contributes to shorter classification time. Furthermore, the selected features can train stable traffic classification generative models irrespective of the dataset's spatial and temporal differences, with consistent accuracy up to 97%. The overall performance indicates that EWA-selected statistical flow features can improve the overall traffic classification.