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This paper focuses on the application of machine learning in cybersecurity and risk prediction. Considering the complexity and dynamics of the cyber environment, the role of plain Bayesian algorithms in improving the accuracy of cybersecurity prediction is explored. First, the limitations of traditional linear regression machine learning algorithms in cybersecurity are analyzed, pointing out that it is difficult for these algorithms to capture complex nonlinear relationships. For this reason, plain Bayes is introduced to improve machine learning for more accurate prediction and assessment of cybersecurity risks by capturing functions and probabilistic agent models. In terms of methodology, this paper combines various technical tools such as statistical analysis, feature extraction, and risk assessment. The research results show that the improved algorithm outperforms the traditional method in simulation experiments, which is reflected in the 10% improvement in risk assessment accuracy and 20% enhancement of detection sensitivity to network intrusion. In addition, through big data analysis, the algorithm successfully identifies key risk points in network security and effectively predicts potential security threats. The improved machine learning algorithm with plain Bayes has significant advantages in network security and risk prediction and can effectively enhance network security protection capability and prediction accuracy. |
