A robust supervised machine learning based approach for offline-online traffic classification of software-defined networking

• Published in: Peer-to-peer networking and applications Vol. 17; no. 1; pp. 479 - 506
• Main Authors: Eissa, Menas Ebrahim, Mohamed, M. A., Ata, Mohamed Maher
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2024; Springer Nature B.V
• Subjects: Classification; Classifiers; Communications Engineering; Communications traffic; Computer Communication Networks; Datasets; Engineering; Information Systems and Communication Service; Machine learning; Networks; Quality of service architectures; Signal,Image and Speech Processing; Software-defined networking; Supervised learning; Software-defined networking; And machine learning; Traffic classification; Quality of service
• ISSN: 1936-6442; 1936-6450
• DOI: 10.1007/s12083-023-01605-7

Due to the exponential increase of internet applications and network users, network traffic classification (NTC) is a crucial study subject. It successfully improves network service identifiability and security concerns of the traffic network and provides a way that improves the Quality of services (QoS). Recently, with the emergence of software-defined networking (SDN) and its ability to get the entire network overview using a centralized controller, machine learning (ML) has been used for NTC. In this paper, an SDN QoS guarantee framework with machine learning traffic classification has been proposed. The framework includes a classification system with two stages, the offline stage, where the classifier was trained and tested, and the online stage, where dealing with the flows and testing the classifier speed is simulated using spark streaming. The result shows that the classifier successfully identifies the specific traffic application with an accuracy of 100% on the “IP-network-traffic-flows-labeled-with-87-apps” dataset and identifies the traffic type with an accuracy of 99.95% on the “ISCX-VPN-NONVPN” dataset. In addition, the classifier speed is proven to be a round 3500 record/sec and a patch duration of 917.3 ms on average with 3210 flows/Trigger.