Network Security Situation Prediction Technology Based on Fusion of Knowledge Graph

Abstract

It is difficult to accurately reflect different network attack events in real time, which leads to poor performance in predicting network security situations. A knowledge graph-based entity recognition model and entity relationship extraction model was developed for enhancing the reliability and processing efficiency of secure data. Then a knowledge graph-based situational assessment method was introduced, and a network security situational prediction model based on self-attention mechanism and gated recurrent unit was constructed. The study's results showed that the constructed prediction model achieved stable mean square error values of approximately 0.0127 and 0.0136 after being trained on the NSL-KDD and CICIDS2017 datasets for 678 and 589 iterations, respectively. The mean square error value was lower due to fewer training iterations compared to other prediction models. The model was embedded into the information security system of an actual Internet company, and the detection accuracy of the number of network attacks was more than 95%. The results of our study indicate that the method used in the study can accurately predict the network security situation and provide technical support for predicting network information security of the same type.