A dynamic network traffic classifier using supervised ML for a Docker-based SDN network
نویسندگان
چکیده
With the rapid technological growth in last decades, number of devices and users has drastically increased. Software-defined networking (SDN) with machine learning (ML) become an emerging solution for network scheduling, quality service (QoS), resource allocations, security. This paper focuses on implementation a traffic classifier using novel Docker-based SDN network. ML offers good performance to real-time solutions without depending well-known TCP or UDP port numbers, IP addresses, encrypted payloads. In this paper, three techniques, we first classify flows 3, 5, 7 parameters giving up 97.14% accuracy. Additionally, present new accelerator algorithm (PAA), which incorporates these classifiers accelerates overall significantly. We then propose dynamic (DNC) generated from PAA over Finally, controller Ryu platforms, integrates DNC classifies both real-time. Based evaluations, improvement latency been demonstrated, where analysing packet, processing time takes average 10 µs. study will certainly serve further research optimising QoS.
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ژورنال
عنوان ژورنال: Connection science
سال: 2021
ISSN: ['0954-0091', '1360-0494']
DOI: https://doi.org/10.1080/09540091.2020.1870437