Research on network security situation awareness algorithm based on Schwartz equation of cloud computing

In this paper, the Schwartz situational awareness database was utilized, with Root Mean Square Error (RMSE) and Absolute Mean Error (AME) as evaluation criteria. Python was employed for simulation, and experimental data were processed and analyzed. The results demonstrate that the network security situational awareness algorithm, constructed using the Item-Based Collaborative Filtering (IBCF) Ridge regression loss function filtering algorithm, the Ridge regression loss function filtering algorithm with Bayesian weight attenuation, the Probability Matrix Decomposition Algorithm (P-variant particle swarm third-order exponent), singular value decomposition, and the C-FOLD (Collaborative Filtering Optimized by Local Distribution) entropy++ Ridge regression loss function filtering Schwartz equation (N-variant particle swarm third-order index), exhibits varying levels of accuracy. IBCF and User-Based Collaborative Filtering (UBCF) showed relatively low accuracy, while the P-variant particle swarm third-order index, with C-FOLD entropy++ achieving the highest accuracy. Additionally, perceptual redundancy was found to influence accuracy, with increasing perceptual redundancy leading to a gradual improvement in accuracy.