Using Machine Learning to Discover Traffic Patterns in Software Defined Networks

Subject Areas : Computer Networks

Pouya Khosraviandehkordi ^{1 *} , Abdulrazzaq Mosa Al-Mhanna ²

1 - گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی، واحد شهرکرد
2 - گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه ازاد اسلامی، واحد خوراسگان، اصفهان، ایران

Received: 2024-08-17 Accepted : 2025-05-23 Published : 2025-06-30

Keywords: Network Traffic, Software-Defined Networks, SDN, Machine Learning,

Abstract :

In this research, we introduce a deep learning model based on Convolutional Neural Networks (CNNs) along with the Bird Swarm Optimization algorithm to identify and discover traffic patterns in Software-Defined Networks (SDNs). The main objective of this study is to investigate the capability of deep learning models in analyzing traffic data and identifying unique patterns present in SDNs. Using a diverse and comprehensive dataset, the proposed model is trained and evaluated. The use of CNNs, due to their layered structure and deep learning capabilities, enables the identification of unique traffic patterns that are prominently visible in SDNs. The proposed model, with high accuracy and good generalization ability, can serve as an effective tool in enhancing the accuracy and efficiency of traffic pattern identification systems in software-defined networks. This research not only demonstrates the superiority of deep learning models in traffic pattern recognition but also provides practical and effective solutions for traffic analysis and management in SDNs. The results of this study indicate that the proposed model achieves an accuracy of 96.5%, suggesting that the proposed method can significantly contribute to the development and improvement of security systems and performance optimization in software-defined networks.

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