A novel research on network security situation prediction based on iteratively optimized RBF-NN

Network security situation (NSS) prediction has attracted significant attention in recent years due to its potential to preemptively mitigate various types of network attacks. However, existing methods still suffer from several drawbacks, including slow convergence, susceptibility to local optima, and limited generalization ability, particularly when dealing with non-stationary and non-linear NSS data. In this paper, we propose a novel iterative optimized RBF-NN method for NSS prediction. Our proposed method leverages a resource allocation network (RAN) to dynamically determine the optimal number of neurons in the hidden layer, ensuring a balance between model complexity and prediction accuracy. Moreover, we introduce a cross-model method with a genetic algorithm to compute the optimal weights for the RBF-NN model. Specifically, we come up with a chaos search strategy during the iterative optimization process to prevent the RBF-NN model from falling into a local extreme point. Experimental results demonstrate that our method achieves a significant improvement in prediction accuracy, with an increase of up to 86.6%, while reducing the training time by up to 29.2% compared to existing techniques. These improvements are achieved within a tolerable training time, making our method both efficient and effective for real-world NSS prediction tasks.