A Hybrid Network Traffic Prediction Model Based on Optimized Neural Network

Abstract

With growth of networks, it's demanding to predict the development of network traffic. In this paper, we analyze the network traffic based on the hybrid neural network model. The chaotic property of traffic data is verified by analyzing the chaos characteristics of the data. Based on the study of artificial neural network, wavelet transform theory and quantum genetic algorithm, we propose a neural network optimization method based on efficient global search capability of quantum genetic algorithm. The proposed quantum genetic artificial neural network model can predict the network traffic more accurately. The prediction results can be used to monitor the network anomaly in network security field, and improve the quality of service. The results will also benefit to search efficient network optimization solutions by predicting network behavior.