Abstract
Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS requirements. However, conventional traffic classification methods, such as the port-based method, are inefficient because of their inability to handle dynamic port allocation and encryption. Traffic classification using machine learning has gained research interest as an alternative method to achieve high performance. In fact, machine learning embeds intelligence into network functions, thus improving network management. In this study, we apply machine learning algorithms to predict network traffic classification. We apply four supervised learning algorithms: support vector machine, random forest, k-nearest neighbors, and decision tree. We also apply a port-based method of traffic classification based on applications’ popular assigned port numbers. Then, we compare the results of this method to those obtained from the machine learning algorithms. The evaluation results indicate that the decision tree algorithm provides the highest average accuracy among the evaluated algorithms, at 99.18%. Moreover, network traffic classification using machine learning provides more accurate results and higher performance than the port-based method.
Highlights
While this method uses only an improved convolutional neural network model to enhance the feature selection, we rely on four supervised machine learning algorithms and compare their results for traffic classification, aiming to improve the Quality of Service (QoS) in smart city networks by classifying the network traffic
We provide a comprehensive study and evaluate the performance of supervised classification algorithms—namely, support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), and decision tree (DT)—to improve the QoS in smart city networks and classify network traffic according to statistical features
We present the evaluation of the machine learning algorithms in terms of performance measures, the impact of the number of classes on accuracy, and their training and execution times
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Differentiated Services aim to improve the scalability problem of Integrated Services They group traffic flows into QoS classes using the Differentiated Services Codepoint field in IPv4 and IPv6 headers to satisfy QoS requirements differently compared with flow-based QoS treatment [8,13]. Traffic classification using machine learning algorithms is more accurate than that using the port-based method. Machine learning algorithms leverage various statistical features in addition to the port number for classification, whereas the port-based method relies solely on assigned port numbers for different applications, which provides ineffective results as many services use dynamic or a variety of ports over a network.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
