A Mathematical Model for Wireless Network Security Posture Prediction Based on Optimized Neural Network Algorithm

ABSTRACT This paper proposes a mathematical model for wireless network security situation prediction based on optimized neural network algorithms to address the complexity and nonlinear characteristics of wireless network environments. Build a predictive index system that includes six indicators, including attack threat level, number of attack sources, and attack frequency, and quantify and process them in a unified data format. Determine the weights of each indicator through gray relational analysis to reflect their impact on the overall security situation. The model adopts an improved BP neural network with a three‐layer structure. The input layer receives situational data, the hidden layer performs feature extraction and mapping, and the output layer provides prediction results. To address the problems of slow convergence and susceptibility to local optima in traditional BP neural networks, the L‐M algorithm is introduced to optimize network parameters. By adaptively adjusting the search direction, the convergence speed and prediction accuracy are improved. The experiment shows that the comprehensive index of network security situation predicted by the model is consistent with the actual scenario and accurately reflects the changes in network security status. Compared with the prediction results before and after optimizing the L‐M algorithm, the optimized model significantly reduces the prediction error, with a maximum error of only 0.01, improving the prediction accuracy.