# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
###### 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. traffic classification; SDN; deep learning; recurrent neural network; GRU; BiGRU; LSTM; BiLSTM +
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changing nature of modern applications. The centralized controller within SDN provides a comprehensive network view, enabling the monitoring, configuration, and control switches to manage the traffic flows [6]. The SDN controller collects information about network flows and offers real-time programmability, making it conducive to integrating ML models. This integration can infuse intelligence into the SDN controller, enabling it to perform tasks such as data analysis, traffic classification, resource management, security, and general network services [7]. However, while the controller enables efficient flow control, it cannot achieve distributed end-to-end QoS management and control attacks due to the lack of control of end users. Hence, incorporating ML techniques into SDN has garnered significant interest as it offers innovative data-driven approaches to tackle conventional network challenges. Traffic classification is a complex endeavor that entails categorizing network traffic into specific classes, serving various objectives, including network management, service measurement, and network monitoring. Additionally, it plays a crucial role in enabling efficient resource allocation and configuring access controls, quality of service (QoS), and other network security parameters. Commonly utilized traffic classification methods include the port-based method [8], statistical approach [9] and Deep Packet Inspection (DPI) [10; 11]. Nevertheless, these traditional techniques have become less effective, with most applications now operating on dynamic ports and network traffic encryption. Hence, there is a need to develop a novel classification technique that is better suited to the current operational conditions. Artificial Intelligence (AI) has become essential for analyzing and sorting network traffic [12]. This technique collects data and applies machine learning algorithms to classify and identify various types of network traffic. It observes patterns and features transmitted within network packets, such as their size, duration, and frequency. Analyzing the contextual information associated with the packets allows for categorizing traffic into specific groups, including encrypted traffic [13; 14].