Network Security Evaluation Based on Improved Genetic Algorithm and Weighted Error Backpropagation Algorithm

Abstract

As the speed advancement of network technology and the popularization of applications, network security problems are becoming more and more prominent, all kinds of network attacks and security threats are increasing, and the demand for network security evaluation is becoming more and more urgent. To address the issues of long time-consuming and low accuracy in the traditional network security evaluation model, the study proposes a network security evaluation model based on improved genetic algorithm and weighted error BP algorithm. The study first combines the weighted error BP algorithm with the improved genetic algorithm for data analysis and research, and then integrates the two to construct a network security evaluation model. The results show that in the detection of network security vulnerabilities, the evaluation model of the data processing vulnerability detection accuracy, risk detection rate of 93.28%, 91.88%, respectively. The function training error of the model is 8.93% respectively, while the decoding accuracy and stability are 90.43% and 92.07% respectively, which are better than the comparison method. This indicates that the method has high accuracy and robustness in network security evaluation, and can provide network administrators and users with a more scientific and reliable basis for decision-making.