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In recent years, the amount of Cyber Security data generated in the form ofunstructured texts, for example, social media resources, blogs, articles, andso on has exceptionally increased. Named Entity Recognition (NER) is an initialstep towards converting this unstructured data into structured data which canbe used by a lot of applications. The existing methods on NER for CyberSecurity data are based on rules and linguistic characteristics. A DeepLearning (DL) based approach embedded with Conditional Random Fields (CRFs) isproposed in this paper. Several DL architectures are evaluated to find the mostoptimal architecture. The combination of Bidirectional Gated Recurrent Unit(Bi-GRU), Convolutional Neural Network (CNN), and CRF performed better comparedto various other DL frameworks on a publicly available benchmark dataset. Thismay be due to the reason that the bidirectional structures preserve thefeatures related to the future and previous words in a sequence.

Deep Learning Approach for Intelligent Named Entity Recognition of Cyber Security