  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.
