Abstract
In order to assist security analysts in obtaining information pertaining to the cybersecurity tailored to the security domain are needed. Since labeled text data is scarce and expensive, Named Entity Recognition (NER) is used to detect the relevant domain entities from the raw text. To train a new NER model for cybersecurity, traditional NER requires a training corpus annotated with cybersecurity entities. Our previous work proposed a Human-Machine Interaction method for semi-automatic labeling and corpus generation for cybersecurity entities. This method requires small dictionary that has the pairs of keywords and their categories, and text data. However, the semantic similarity measurement in the method to solve the ambiguous keywords requires the specific category names even if non cybersecurity related categories. In this work, we introduce another semantic similarity measurement using text category classifier which does not require to give the specific non cybersecurity related category name. We compare the performance of the two semantic similarity measurements, and the new measurement performs better. The experimental evaluation result shows that our method with the training data that is annotated by small dictionary performs almost same performance of the models that are trained with fully annotated data.
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Kashihara, K., Sandhu, H.S., Shakarian, J. (2022). Automated Corpus Annotation for Cybersecurity Named Entity Recognition with Small Keyword Dictionary. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_11
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