Deep Learning Approach for Intelligent Named Entity Recognition of Cyber Security
This work addresses the need for structured data extraction in cyber security, though it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the problem of Named Entity Recognition (NER) for unstructured cyber security data by proposing a deep learning approach with Conditional Random Fields, finding that a combination of Bi-GRU, CNN, and CRF performed better than other frameworks on a benchmark dataset.
In recent years, the amount of Cyber Security data generated in the form of unstructured texts, for example, social media resources, blogs, articles, and so on has exceptionally increased. Named Entity Recognition (NER) is an initial step towards converting this unstructured data into structured data which can be used by a lot of applications. The existing methods on NER for Cyber Security data are based on rules and linguistic characteristics. A Deep Learning (DL) based approach embedded with Conditional Random Fields (CRFs) is proposed in this paper. Several DL architectures are evaluated to find the most optimal architecture. The combination of Bidirectional Gated Recurrent Unit (Bi-GRU), Convolutional Neural Network (CNN), and CRF performed better compared to various other DL frameworks on a publicly available benchmark dataset. This may be due to the reason that the bidirectional structures preserve the features related to the future and previous words in a sequence.