To solve the problems of insufficient data sources and strong entity professionalism in the field of threat intelligence,AbNER,a named entity recognition model for threat intelligence for small samples was proposed.An implicit feature guidance module based on the self-attention mechanism was designed,a prompt template was introduced to integrate the prior knowledge of the professional field,and the two were combined to jointly complete the identification of entities.The model input layer struc-ture was optimized to effectively improve the coding performance.The test results of AbNER on two types of threat intelligence data,such as general and power grid,were analyzed.The model achieves the best performance on 5 full data sets and 3 small sample data sets,which verifies the entity recognition advantages and small sample capabilities of AbNER.
关键词
命名实体识别/威胁情报/小样本/自注意力机制/大规模语言模型/提示学习/网络安全
Key words
named entity recognition/threat intelligence/small samples/self-attention mechanisms/large language model/prompt learning/cybersecurity
维普
万方数据