Visible to the public A Self-Attention-Based Approach for Named Entity Recognition in Cybersecurity

TitleA Self-Attention-Based Approach for Named Entity Recognition in Cybersecurity
Publication TypeConference Paper
Year of Publication2019
AuthorsLi, Tao, Guo, Yuanbo, Ju, Ankang
Conference Name2019 15th International Conference on Computational Intelligence and Security (CIS)
KeywordsBiLSTM, Collaboration, composability, computer security, Context modeling, CRF, cybersecurity, entity recognition, feature extraction, Hidden Markov models, Human Behavior, Labeling, machine learning, Metrics, Policy-Governed Secure Collaboration, pubcrawl, resilience, Resiliency, Scalability, science of security, Self-Attention mechanism
AbstractWith cybersecurity situation more and more complex, data-driven security has become indispensable. Numerous cybersecurity data exists in textual sources and data analysis is difficult for both security analyst and the machine. To convert the textual information into structured data for further automatic analysis, we extract cybersecurity-related entities and propose a self-attention-based neural network model for the named entity recognition in cybersecurity. Considering the single word feature not enough for identifying the entity, we introduce CNN to extract character feature which is then concatenated into the word feature. Then we add the self-attention mechanism based on the existing BiLSTM-CRF model. Finally, we evaluate the proposed model on the labelled dataset and obtain a better performance than the previous entity extraction model.
Citation Keyli_self-attention-based_2019