A Network Security Situation Prediction Method Based on SSA-GResNeSt

Convolutional neural networks have been widely used in intrusion detection and proactive network defense strategies such as network security situation prediction (NSSP). The interaction between cross-channel features and the dependencies between elements in the input data are essential factors that affect the prediction model's performance. However, existing works have ignored these, resulting in performance that needs to be improved. To this end, we propose a GResNeSt model that combines the advantages of the global context block and ResNeSt to improve the NSSP performance. The GResNeSt model strengthens traditional convolutional neural networks in two ways: it effectively captures cross-feature interactions and obtains long-range dependencies of the input data. This enhances its performance in capturing associations among different elements, making it more effective in extracting critical information from data to identify network attacks. We used the Salp swarm algorithm to select optimal hyperparameters for improving the model's performance. Furthermore, based on the attack impact, we calculated network security situation values of two public network datasets. Finally, comprehensive experiments on the datasets verified our model design and demonstrated that our scheme is superior to other models in terms of NSSP ability.