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

Abstract

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&#x0025;, decreases packet loss, delay, and QoS violations by 51&#x0025;, 21&#x0025;, and 52&#x0025;, 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 <inline-formula><tex-math notation="LaTeX">$F1$</tex-math></inline-formula>-score, as well as lower loss values, compared to the multilayer perceptron classifier.