A dynamic network traffic classifier using supervised ML for a Docker-based SDN network. Issue 3 (3rd July 2021)

• Record Type: Journal Article
• Authors: Mondal, Pritom Kumar; Aguirre Sanchez, Lizeth P.; Benedetto, Emmanuele; Shen, Yao; Guo, Minyi
• Is Part Of: Connection science. Volume 33:Issue 3(2021)
• Page Start: 693
• Page End: 718
• Publication Date: 2021-07-03
• Subjects: Quality of service -- software-defined network -- traffic classification -- machine learning; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2020.1870437
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : With the rapid technological growth in the last decades, the number of devices and users has drastically increased. Software-defined networking (SDN) with machine learning (ML) has become an emerging solution for network scheduling, quality of service (QoS), resource allocations, and security. This paper focuses on the implementation of a network traffic classifier using a novel Docker-based SDN network. ML offers good performance to real-time traffic solutions without depending on well-known TCP or UDP port numbers, IP addresses, or encrypted payloads. In this paper, using three ML techniques, we first classify network flows with 3, 5, and 7 parameters giving up to 97.14% accuracy. Additionally, we present a new performance accelerator algorithm (PAA), which incorporates these three ML classifiers and accelerates the overall performance significantly. We then propose a dynamic network classifier (DNC) generated from PAA over a novel Docker-based SDN network. Finally, we propose a new controller algorithm for Ryu platforms, which integrates the DNC and classifies both TCP and UDP flows in real-time. Based on the evaluations, an improvement in latency performance has been demonstrated, where analysing a packet, controller processing time takes on an average of 10 µs. This study will certainly serve to further research on optimising SDN and QoS.