Biblio

With the increase of the number of network threats, the knowledge graph is an effective method to quickly analyze the network threats from the mass of network security texts. Named entity recognition in network security domain is an important task to construct knowledge graph. Aiming at the problem that key Chinese entity information in network security related text is difficult to identify, a named entity recognition model in network security domain based on BERT-BiLSTM-CRF is proposed to identify key named entities in network security related text. This model adopts the BERT pre-training model to obtain the word vectors of the preceding and subsequent text information, and the obtained word vectors will be input to the subsequent BiLSTM module and CRF module for encoding and sorting. The test results show that this model has a good effect on the data set of network security domain. The recognition effect of this model is better than that of LSTM-CRF, BERT-LSTM-CRF, BERT-CRF and other models, and the F1=93.81%.

The borderless, dynamic, high dimensional and virtual natures of cyberspace have brought unprecedented hard situation for defenders. To fight uncertain challenges in versatile cyberspace, a security framework based on the cloud computing platform that facilitates containerization technology to create a security capability pool to generate and distribute security payload according to system needs. Composed by four subsystems of the security decision center, the image and container library, the decision rule base and the security event database, this framework distills structured knowledge from aggregated security events and then deliver security load to the managed network or terminal nodes directed by the decision center. By introducing such unified and standardized top-level security framework that is decomposable, combinable and configurable in a service-oriented manner, it could offer flexibility and effectiveness in reconstructing security resource allocation and usage to reach higher efficiency.