Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
TABLE II: evaluation results (micro average for 11 labels)
['Models', 'English Dataset Precision', 'English Dataset Recall', 'English Dataset F1-score', 'Chinese Dataset Precision', 'Chinese Dataset Recall', 'Chinese Dataset F1-score']
The number of extracted contextual keywords from the English dataset is 1,328, and from the Chinese dataset is 331. Furthermore, we quantitatively compare our study with other typical works of sequence labelling, We train these models by employing the same training set and training parameters as the proposed model. The performance gain of the proposed model on the Chinese dataset is approximately 4.2% of precision and 9.0% of recall. on the English dataset and significantly higher F1-score on the Chinese dataset.