Joint extraction of Chinese cybersecurity events based on bidirectional TCN and attention

Yonggan Zhang; Fangjie Wan; Danyang Yang

doi:10.1117/12.2646773

7 September 2022 Joint extraction of Chinese cybersecurity events based on bidirectional TCN and attention

Yonggan Zhang, Fangjie Wan, Danyang Yang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290Y (2022) https://doi.org/10.1117/12.2646773
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Cybersecurity event extraction aims to extract threat intelligence information from unstructured text and provide a data basis for correlation analysis of cybersecurity events. However, in the field of cybersecurity, the complexity of terminology in event data as well as the problem of polysemy and crossover between Chinese and English in Chinese texts pose great challenges to event extraction. Existing methods usually perform event extraction in a pipelined manner, ignoring the dependencies between event elements and event triggers. Therefore, a joint event extraction model is proposed in this paper. The model captures the contextual features of sentences in the encoding layer by an improved temporal convolutional network (TCN) and then enhances the dependency features between triggers and arguments using a multihead attention mechanism to achieve sentence-level joint extraction of Chinese cybersecurity events. We conducted comparison experiments on real cybersecurity news data to evaluate the event extraction performance of the model. Compared with a baseline model LSTM, the method improves the F1 values by 13.2% and 33.2% in two subtasks of trigger extraction and argument extraction, respectively.

Citation Download Citation

Yonggan Zhang, Fangjie Wan, and Danyang Yang "Joint extraction of Chinese cybersecurity events based on bidirectional TCN and attention", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290Y (7 September 2022); https://doi.org/10.1117/12.2646773

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KEYWORDS

Feature extraction

Data modeling

Convolution

Performance modeling

Computer programming

Matrices

Feature selection

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