Cybersecurity Named Entity Recognition Using Bidirectional Long Short-Term Memory with Conditional Random Fields

Network texts have become important carriers of cybersecurity information on the Internet. These texts include the latest security events such as vulnerability exploitations, attack discoveries, advanced persistent threats, and so on. Extracting cybersecurity entities from these unstructured texts is a critical and fundamental task in many cybersecurity applications. However, most Named Entity Recognition (NER) models are suitable only for general fields, and there has been little research focusing on cybersecurity entity extraction in the security domain. To this end, in this paper, we propose a novel cybersecurity entity identification model based on Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM with CRF) to extract security-related concepts and entities from unstructured text. This model, which we have named XBiLSTM-CRF, consists of a word-embedding layer, a bidirectional LSTM layer, and a CRF layer, and concatenates X input with bidirectional LSTM output. Via extensive experiments on an open-source dataset containing an office security bulletin, security blogs, and the Common Vulnerabilities and Exposures list, we demonstrate that XBiLSTM-CRF achieves better cybersecurity entity extraction than state-of-the-art models.

Automated summary

Network texts play a crucial role in conveying cybersecurity information, and extracting cybersecurity entities is essential for cybersecurity applications. This paper introduces a novel cybersecurity entity identification model based on Bidirectional Long Short-Term Memory with Conditional Random Fields, showing superior performance in cybersecurity entity extraction through experiments on various types of security-related texts.