Cyber security entity recognition method based on residual dilation convolution neural network

Abstract

In recent years, cybersecurity threats have increased, and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity. In particular, the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data. Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs. The composition of security entities in open network text data is very complex, which makes traditional deep learning methods difficult to identify accurately. Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers), a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed. The BERT model was used to train the character-level feature vector representation. Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity, and finally obtain the BIO annotation of each character through CRF. Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model, the BiLSTM-CRF model and the traditional entity recognition model.

Keywords

• cybersecurity
• entity recognition
• residual connection
• dilation convolution neural network
• bert pre-train model