Analysis and Research of Network Information Security Evaluation Model Based on Machine Learning Algorithm

Abstract

Due to the rapid development of network technology and the rapid spread of information through the network, the security of information systems is also threatened in many aspects. The purpose of this study is to build a security evaluation model of network information based on machine learning algorithms and to improve the model and the accuracy of model evaluation. Firstly, a hierarchical network information security assessment index system is constructed. Secondly, to improve the accuracy of security assessment, an improved AFSA algorithm and TWSVM model are introduced for enhancing classification accuracy. A security assessment based on improved AFSA-TWSVM is proposed to evaluate the model. Finally, the experiments are carried out to compare with the AFSA-SVM-based security assessment model and the PSO-LLSVM security assessment model. The experimental results show that the average classification accuracy of the AFSA-SVM-based security assessment model and the PSO-LLSVM-based security assessment model is S7.5 and 83.33%, respectively. The average classification accuracy of the improved AFSA-TWSVM reaches 90%, which is better than the other two evaluation models in classification accuracy. Therefore, the model proposed in this study is more suitable for network information security evaluation.