Research on Network Information Security Situation Awareness and Prediction Model Based on Deep Learning

With the continuous evolution of network attack methods, traditional network security protection technologies are no longer effective in dealing with complex and ever-changing attacks. This study proposes a deep learning (DL) based network information security situational awareness and prediction model, which combines the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM), aiming to achieve real-time situational awareness of the network environment and accurate prediction of future security threats. Firstly, the original network data is preprocessed, and then the spatial characteristics of the data are automatically extracted by CNN. Then, the time series analysis is carried out by using LSTM to capture the time dependence in the data and realize situation awareness. In addition, the model also constructs a situation prediction module based on LSTM to predict the future network security situation by learning the time series law of historical situation data. The experimental results show that the model achieves 92.5% situational awareness accuracy on the test set, especially in identifying high-level security threats. Short-term prediction shows high accuracy, but long-term prediction accuracy gradually decreases with the increase of time step. The model still maintains high situational awareness accuracy after introducing noise and abnormal data, showing good robustness. Although the model has some shortcomings, such as high consumption of computing resources and low accuracy of long-term prediction, model optimization and data set expansion are expected to further improve the performance of the model and provide new ideas and methods for building an intelligent and efficient network security protection system.