Simulation of Network Security Situation Assessment Model Based on Machine Learning Algorithm
H. Y. Fu, Meiling Liu, Yue Zou · 2023
While the network has gained popularity, it has also been attacked directly or indirectly by some lawless elements’ current technical means. Network Security Situation Awareness (NSSA) system can collect the relevant factors that affect network security, and analyze and process them, so as to infer the future network change trend, which can help network administrators predict the network development trend and make relevant countermeasures in time to prevent it. In this paper, the assessment model of network security situation (NSS) based on machine learning algorithm is studied, and the support vector machine (SVM) method is proposed to improve the accuracy of NSSA. The optimized prediction model is used to predict NSS, and the optimal prediction value is obtained. Finally, its performance is simulated and tested. The test results show that with the increase of the number of experiments, the assessment accuracy of the model is stable at around 94% and tends to a stable state. By comparing the accuracy of ID3 algorithm with that of traditional neural network (NN) algorithm, the accuracy of the algorithm proposed in this paper is much higher than that of the comparison algorithm. The proposed method can improve the speed and accuracy of NSS assessment and prediction, which shows the feasibility of this method.