LAUSR

Network security situation assessment is an important means of understanding the current network security situation to provide a basis for taking security measures. To address the problem that the accuracy of existing network security situation assessment methods needs to be improved, this paper proposes a network security situation assessment method based on support vector machine (SVM) optimized by whale optimization algorithm (WOA) that is improved by adaptive weight (AW) combined with simulated annealing algorithm (SA). In this method, the SVM is embedded into the fitness function calculation of the improved WOA, and the global optimization characteristics of WOA are used to determine the optimal penalty parameter $c$ and kernel function parameter $g$ of the SVM. To solve the problem of the WOA being prone to falling into local extremum and slow convergence when solving large and complex data problems, an adaptive weight is used to adjust the whale position update coefficient, and a simulated annealing algorithm (SA) is used to increase random search factors to avoid falling into local extremum, so as to improve the global optimization ability. The experimental results show that this method is feasible, can assess the network security situation more accurately, and has better convergence than other assessment algorithms based on an improved SVM.