A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

Daniel Nuñez-Agurto (), Walter Fuertes, Luis Marrone, Eduardo Benavides-Astudillo, Christian Coronel-Guerrero and Franklin Perez
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Daniel Nuñez-Agurto: Department of Computer Science, Universidad de las Fuerzas Armadas—ESPE, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador
Walter Fuertes: Department of Computer Science, Universidad de las Fuerzas Armadas—ESPE, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador
Luis Marrone: Faculty of Computer Science, Universidad Nacional de La Plata, La Plata 1900, Argentina
Eduardo Benavides-Astudillo: Department of Computer Science, Universidad de las Fuerzas Armadas—ESPE, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador
Christian Coronel-Guerrero: Department of Computer Science, Universidad de las Fuerzas Armadas—ESPE, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador
Franklin Perez: Department of Computer Science, Universidad de las Fuerzas Armadas—ESPE, Av. General Rumiñahui S/N, Sangolquí 171103, Ecuador

Future Internet, 2024, vol. 16, issue 5, 1-23

Abstract: The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment.

Keywords: traffic classification; SDN; deep learning; recurrent neural network; GRU; BiGRU; LSTM; BiLSTM (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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