Abstract
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.
