A Cyberspace Security Entity Recognition Method Integrating GCNN and GRU
The cyberspace is an open environment,it allows users to freely communicate,share resources,and engage in online activities.This openness makes the network vulnerable to attacks by hackers.By identifying cyberspace security entities,their behavior patterns and characteristics can be analyzed to predict and prevent future cyber attacks.To this end,a cyberspace se-curity entity recognition method integrating generalized congruence neural network(GCNN)and gated recurnent unit(GRU)is studied.A bidirectional encoder representations from transformers(BERT)pretrained language model is established to extract word embeddings,segment embeddings and positional embeddings from text sentences,and obtain feature information from the corpus as well as the specific positions of entities.The context semantic information of the text sentence is obtained by bidirec-tional GRU,and after the convolution operation by GCNN,the optimal feature vector is output,which is the result of cyber-space security entity recognition.The experimental results show that the accuracy and recall of the proposed method are always above 95％,and the F1 value is closest to 1.The cyberspace security entity recognition results are accurate,and can effectively protect network security.
万方数据
维普
