A Novel Deep-Learning-Enabled QoS Management Scheme for Encrypted Traffic in Software-Defined Cellular Networks

• Published in: IEEE systems journal 2022-06, Vol.16 (2), p.1-12
• Main Authors: Mahboob, Tahira, Lim, Jae Won, Shah, Syed Tariq, Chung, Min Young
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Cellular communication; Classifiers; Computer networks; Cryptography; Deep learning; Encrypted traffic classification; Feature extraction; Logic gates; Multilayer perceptrons; network management; Neural networks; Neurons; over-the-top (OTT) services; Over-the-top media services; Quality of service; quality of service (QoS); Quality of service architectures; Radio equipment; Service introduction; software-defined cellular networks (SDCNs); Software-defined networking
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2021.3089175

Mobile users are served with over-the-top (OTT) services through their cellular networks. To ensure the privacy of users and confidentiality of content, most OTT service providers encrypt their traffic. When a cellular network has no information about the type of service, a default bearer may be created. However, the default bearer may not guarantee bandwidth to a service. Therefore, users may experience degraded service due to packet loss, delay, and reduced data rates. This article proposes a novel quality-of-service (QoS) management scheme for encrypted traffic in software-defined cellular networks. We introduce a deep-learning-enabled intelligent gateway to predict the service types of encrypted flows by considering statistical and QoS features. A QoS control manager maps the bearers to ongoing flows satisfying their QoS requirements. As a proof of concept, we implement a testbed considering encrypted traffic from the Tor network. Results indicate that the proposed scheme improves the network throughput by 41%, decreases packet loss, delay, and QoS violations by 51%, 21%, and 52%, respectively, and reduces the length and size of the queue at the base station compared to those of the conventional scheme. Moreover, the convolutional-neural-network-based classifier achieves higher accuracy, precision, recall, and F1 -score, as well as lower loss values, compared to the multilayer perceptron classifier.