Long-Term Prediction of Network Security Situation Through the Use of the Transformer-Based Model

In recent years, the Internet has shown rapid development, and network security issue has gradually become the focus of research by scholars and enterprises. Network security time series is a reliable source to obtain future network security situation, so as to develop network security defense strategy by exploring the correlation of time series. The network security time series is a reliable source to obtain the future network security situation, and it is the main direction of current network security defense by exploring the correlation of time series, and analyzing the future network security situation so as to formulate network security defense strategies. This is the main direction of network security defense. The existing research focuses on the short-term prediction of network attacks, and the robustness and accuracy of long-term prediction still have big problems. To fuse the information from different data sources and capture the correlation between sequences, we design a data source selection module based on the similarity of measurement curves. We then model the network security situation prediction based on deep learning models and propose a situation prediction model based on Temporal Convolutional Network (TCN)-combined Transformer, which focuses on the time series long-term prediction problem, combining the network condition and attack situation to obtain the future network security situation. Our proposed model is divided into three parts, which are the information encoding module, the information synthesis module, and situation value calculation and prediction accuracy evaluation module. The selected multi-dimensional situations element data are used as model input, and the TCN-combined Transformer is employed as the network security situational data processing unit to complete the information fusion and prediction tasks. Finally, the role of data source selection on prediction accuracy is evaluated using an ablation study. We experimented and evaluated the model at different prediction horizon lengths using five existing baseline models and three performance metrics. The experimental results show that our proposed prediction model has better robustness and accuracy in most of the metrics.