ABSTRACT With the digital transformation of industrial enterprises, Network Security Situation (NSS) assessment and prediction have become key tasks to ensure enterprise information security. To evaluate and predict the NSS more accurately, this study proposes an SAE‐DBSCAN algorithm and an improved Bi‐LSTM. Research showed that the ACA of the SAE‐DBSCAN on different data was 91.3% and 90.7%, which was numerically better than the comparative models. The precision and recall rates after improved Bi‐LSTM convergence were 0.977 and 0.938. When this model was applied to different NSSs, its predicted value was closest to the real value. Especially in the DoS attack type, the difference between the predicted and real values was only 0.008. The outcomes demonstrate that SAE‐DBSCAN can effectively assess the NSS, the improved Bi‐LSTM's performance is better, and the prediction ability is better. This study can help enterprises more accurately identify and respond to potential network security threats, thereby optimizing resource allocation and reducing security risks.
Wáng et al. (Wed,) studied this question.