Situation element extraction of network security based on Logistic Regression and Improved Particle Swarm Optimization

Situation element extraction of network security situation awareness can be transformed into the vast amounts of data recognition and classification. Due to the difficulty of situation element extraction of network security situation awareness, a mechanism for situation extraction based on Logistic Regression (LR) and Improved Particle Swarm Optimization (LR-IPSO) model is proposed. In order to improve local and global search capability of Particle Swarm Optimization(PSO), this paper takes the nonlinear decreasing random strategy for weight value to improve PSO, because of the inherent implicit parallelism and good global optimization ability of IPSO, it is used to estimate parameters and optimize the learning ability of the LR model. Experiment results show that this model is an effective extraction technology of situation element.