VTion-PatchTST: Elevated PatchTST model for network security situation prediction

The rapid development of network attack techniques increases the risk of the internet being attacked, thus necessitating timely and effective network protection mechanisms. Network security situation prediction is a proactive defense method, the results of which can help formulate defense strategies in advance. The current network security situation prediction mainly focuses on single-step prediction, and the accuracy of multi -step prediction needs to be improved. By introducing three technologies: Variational Mode Decomposition (VMD), Temporal Convolutional Networks (TCN), Feature Fusion, an improved PatchTST model is proposed to solute the issue. VMD decomposes a complex sequence of historical situational values into multiple relatively stable subsequences with different features to make the model channel modeling specific. TCN enhances the ability of the model to extract temporal features. Feature fusion makes predictions more comprehensive through cross -channel linking. Both experiments and evaluations are conducted on the UNSW-NB15 and CIC-IDS2017 public datasets, and nine baseline models are used to compare with the proposed VTion-PatchTST. The experimental results show that the proposed model is more applicable to cybersecurity situation forecasting, with an relative average reduction of 40.3% and 29.0% in Mean Square Error (MSE) and Mean Absolute Error (MAE), respectively.