Network Traffic Identification Algorithm Based on Neural Network

In this paper, a network traffic identification model is established using a multilayer excitation function quantum neural network which is suitable for data classification. Because the conventional quantum neural network has different target function in the training of the weights of the network and the sigmoid function of the neurons in the hidden layer, the coupling effect of the two parameters is not processed. This will result in the middle and later stage of the training iteration process, and it may be possible to reduce the objective function value of a kind of parameter, and make the objective function value of another kind of parameter increase. In order to avoid this situation, using LM algorithm to optimize, using the same objective function not only as the target function of the network weight, but also the function of translational spacing of sigmoid function of neurons in the hidden layer, and the training objective is to minimize the sum of squared error of the neural network output and the desired value. Finally, the recognition performance of the proposed algorithm is compared with that of the conventional quantum neural network and LM-BP neural network. The results show that the convergence rate of the proposed algorithm is the fastest and the convergence accuracy is the highest.