Aiming at the problem that the full flow detection mode is easy to cause the performance bottleneck of the security detection equipment, an improved dingo optimization algorithm is given to optimize the radial basis function RBF neural network for normal traffic filtering. First, the wild dog optimization algorithm was improved using Singer chaotic mapping and search balance strategy; second, the output weights of the RBF neural network were optimized with the improved wild dog optimization algorithm, and the network was trained using the CSE-CIC-IDS2018 dataset to construct a normal traffic filtering model. Finally, before the network traffic entered the security detection device, filter out as many normal traffic as possible to reduce the workload of the security detection device. The experimental results show that compared with the existing models, the normal traffic filtering model of IDOA-RBF neural network has a great improvement in modeling time, while maintaining a high recognition accuracy, and can filter out 72.9% of the normal traffic in the traffic to be detected.