““A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity””

First authored paper accepted to the IEEE Big Data 2020 Conference.

Named Entity Recognition (NER) is important in the cybersecurity domain. It helps researchers extract cyber threat information from unstructured text sources. The extracted cyber-entities or key expressions can be used to model a cyber-attack described in an open-source text. A large number of general-purpose NER algorithms have been published that work well in text analysis. In this paper, we compare the recent deep learning-based NER algorithms on a cybersecurity dataset. We created a cybersecurity dataset collected from various sources, including “Microsoft Security Bulletin” and “Adobe Security Updates”. Some of these approaches proposed in literature were not used for Cybersecurity. Others are innovations proposed by us. This comparative study helps us identify the NER algorithms that are robust and can work well in sentences taken from a large number of cybersecurity sources. We tabulate their performance on the test set and identify the best NER algorithm for a cybersecurity corpus. We also discuss the different embedding strategies that aid in the process of NER for the chosen deep learning algorithms.

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Recommended citation:

Dasgupta, Soham, et al. “A comparative study of deep learning based named entity recognition algorithms for cybersecurity.” 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020.