A novel traffic classification method using multi-source Transfer Learning in Distributed SDN networks
DOI:
https://doi.org/10.54654/isj.v2i19.990Keywords:
traffic classification, SDN, transfer learning, TrAdaBoost, multisource TrAdaBoostTóm tắt
Abstract—Network traffic classification (TC) is a critical task in network management, security and information security. As network encryption becomes more popular, TC-based machine learning methods have shown great performance, compared to other TC approaches such as port-based or payload inspection. Besides, recent studies on Software-defined networking (SDN) architecture have addressed the data consistency problem in distributed SDN. This means that the TC problem in distributed SDN with multiple domains can now be considered as one domain. Nevertheless, when a new SDN domain is added to the distributed system, the lack of network data on this domain is inevitable. This can make it difficult to train a good TC model for the new domain due to the absence of a training dataset. To address the problem of insufficient training data in a new SDN domain, this paper proposes a algorithm, called MMSTrAda (modified multiple source TrAdaBoost), a transfer learning method that utilizes knowledge already learned from exsisting SDN domains to improve the performance of the TC model in a new domain. Specifically, our proposal is based on a Multisource TradaBoost algorithm that takes advantage of useful data from various source domains. The experimental results show that the TC model in a new domain based on our proposal achieves about 88% macro-F1, when detecting three popular network services: E-commerce, Interactive data, and Video on-demand.
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