Intrusion Detection Method of BP Neural Network Based on Crow Search Algorithm

In order to improve the accuracy of the intrusion detection system, a back propagation neural network model based on the crow search algorithm (CSA-BP) is proposed. BP neural network is an important method to solve nonlinear problems, but its predictive ability is easily affected by the initial parameters. To solve this problem, the relative percentage error is used as the objective function of the model, and the optimal weight and threshold are found through the strong global search ability of the crow search algorithm. Then, the CSA-BP model is validated with five standard datasets. Finally, the CSA-BP algorithm is used in the intrusion detection system. The results show that the proposed algorithm makes the intrusion detection system more accurate, reaching 96.6%, and speeds up the convergence.