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.

Efficient and scalable network architectures are essential for enterprise environments that demand reliable and high-performance connectivity. Virtual Extensible LAN (VXLAN) technology enables the encapsulation of Layer 2 traffic over Layer 3 infrastructures, while Software-Defined Networking (SDN) introduces centralized control and programmability by decoupling the control and data planes. This study presents the design and implementation of an enterprise network architecture that integrates SDN and VXLAN technologies to enhance scalability, performance, and security. The PPDIOO lifecycle methodology was adopted as a structured framework for design, deployment, and optimization. The proposed architecture was implemented and validated in a GNS3 emulated environment using OpenDaylight as the SDN controller and Open vSwitch (OVS) as the virtual switching layer. Functional testing, including connectivity verification and packet flow analysis, confirmed correct operation of VXLAN tunneling under SDN control, demonstrating the feasibility and effectiveness of the proposed model. The results highlight that the integration of SDN and VXLAN provides a flexible, programmable, and secure foundation for next-generation enterprise network infrastructures.