基于 ALBERT 的网络威胁情报命名实体识别..

Cyber threat intelligence entity identification is the key to cyber threat intelligence analysis. In view of the fact that traditional word embedding cannot represent the polysemy of a word, it is difficult to effectively identify the key information of cyber threat intelligence entities, and at the same time, facing the exponential growth of threat intelligence, the efficiency of the identification model needs to be improved urgently. A network threat intelligence named entity recognition model based on ALBERT is proposed. The model first uses ALBERT to extract threat intelligence dynamic feature word vectors. Then, the feature word vector is input to the bidirectional long short-term memory network (BiLSTM) layer to obtain the corresponding label of each word in the sentence. Finally, the conditional random field (CRF) layer is modified and the sequence label is output with the maximum probability. The experimental results of identification model comparison show that the F1 value of the proposed model is 92.21%, which is obviously better than other models. In the case of the same recognition accuracy, the time and resource costs of the proposed model are also lower, which is suitable for massive and efficient entity recognition tasks in the field of cyber threat intelligence. [ABSTRACT FROM AUTHOR]