A neuro-evolutionary approach for software defined wireless network traffic classification

Abstract

Accurate network traffic classification is an essential and challenging issue for wireless network management and survivability. Existing network traffic classification algorithms, on the other hand, cannot meet the required specifications of real networks' in terms of user privacy control overhead, latency, and above all, classification speed. For wireless network traffic classification, machine learning-based and hybrid optimization techniques have been deployed. This paper takes a software-defined wireless network (SDWN) architecture for network traffic classification into account. Because the proposed scheme is perfectly contained within the network controller,the SDWN controller's higher processing capability, global visibility, and programmability can be used to achieve real-time, adaptive, and precise traffic classification. In this paper, a neuro-evolutionary approach is proposed in which the feed forward neural network (FFNN) is the base classifier and particle swarm optimization (PSO) is used to train the FFNN to accurately classify traffic while minimizing communication overhead between the controller and the SDWN switches. Simulation experiments were conducted by acquiring real-world internet datasets to test the efficacy of the proposed scheme. The results and the state-of-the-art comparisons show that the proposed approach has outperformed in terms of accuracy in wireless traffic classification.