Hanks, CaseyMaiden, MichaelRanade, PriyankaFinin, TimJoshi, Anupam2022-06-212022-06-212022-04-18http://hdl.handle.net/11603/25006Named Entity Recognition (NER) is a critical component of automated knowledge extraction. It allows Natural Language Processing (NLP) models to label instances of real-world entities that are important in the context of the text. To be able to accomplish this, the NLP model needs to be trained on large corpora of human-annotated text. There are examples of general, domain-agonistic text corpora available, but they are not suited for fields such as cybersecurity, that require domain-specific text for downstream tasks such as malware analysis. NLP for cybersecurity is an emerging field, and there is a large need to develop community-accessible datasets to train existing AI-based cybersecurity pipelines to extract meaningful insights from Cyber Threat Intelligence (CTI). There are terabytes of CTI data that are disclosed on a daily basis, making it nearly impossible for human-analysts to manually sift through. The cybersecurity domain has limited training datasets available, as opposed to other domains such as Medicine or Law. We have created a large CTI corpus and are actively using it to train and test supervised and semi-supervised cybersecurity NER models using the SpaCy NLP Framework. In addition, we also aim to develop methods that allow continuous integration of incoming, up-to-date CTI information.5 minutes 36 secondsen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.UMBC Undergraduate Research and Creative Achievement DayUMBC Ebiquity Research GroupSound