Current network security situation assessment methods have limited feature extraction capabilities and can be more efficient. This paper presents a network security situation assessment method that uses a parallel feature extraction network (PFEN) and an improved bi-directional gate recurrent unit (BiGRU). A deep learning model is designed with a PFEN and a BiGRU based on an attention mechanism (ABiGRU). The PFEN module has parallel sparse auto-encoders which identify key data out of the network traffic and integrate this data with the original features. Then, the ABiGRU module weights the key features through the attention mechanism to improve the model accuracy. The trained PFEN-ABiGRU is then applied to network threat detection. The model detection results are combined with a network security quantification method to calculate a network security situation index. Tests indicate that the PFEN-ABiGRU assessments have better accuracy and recall rates than other model assessment results.

Key node recognition methods for complex networks often have insufficient resolution and accuracy. This study developed a K-shell based key node recognition method for complex networks that first stratifies the network to obtain the K-shell (K_s) values for each node that indicate the influence of the global structure of the complex network. A comprehensive degree (CD) was then defined that balances the various influences of neighboring nodes and sub-neighboring nodes. A dynamic adjustable influence coefficient, μ_i, was also defined. Nodes with the same K_s but larger comprehensive degrees are more important. Tests show that this method more effectively identifies key nodes than several classical key node recognition methods and a risk assessment method, and has high accuracy and resolution in different complex networks. This method provides network node risk assessments that can be used to protect important nodes and to determine the risk disposal priority of the network nodes.