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  <title><![CDATA[Recognizing and Extracting Cybersecurity Entities from Text]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1028/Recognizing-and-Extracting-Cybersecurity-Entities-from-Text</link>
  <description><![CDATA[Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong need to develop community-accessible datasets to train existing AI-based cybersecurity pipelines to efficiently and accurately extract meaningful insights from CTI. We have created an initial unstructured CTI corpus from a variety of open sources that we are u...]]></description>
  <dc:date>2022-07-17</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1017/CyberEnt-Extracting-Domain-Specific-Entities-from-Cybersecurity-Text">
  <title><![CDATA[CyberEnt: Extracting Domain Specific Entities from Cybersecurity Text]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1017/CyberEnt-Extracting-Domain-Specific-Entities-from-Cybersecurity-Text</link>
  <description><![CDATA[We have created an initial large, unstructured CTI corpus from a variety of open sources such as cybersecurity vendor reports/blogs, vulnerability databases (Common Vulnerabilities and Exposures (CVE)) records, and Advanced Persistent Threat (APT) reports. We are using the corpus to train and test cybersecurity entity models using the SpaCy framework and, in particular, exploring self-learning methods to automatically recognize cybersecurity entities based on limited but high-quality training...]]></description>
  <dc:date>2022-04-30</dc:date>
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  <title><![CDATA[CyberEnt: A Cybersecurity Domain Specific Dataset for Named Entity Recognition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1022/CyberEnt-A-Cybersecurity-Domain-Specific-Dataset-for-Named-Entity-Recognition</link>
  <description><![CDATA[Named 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 t...]]></description>
  <dc:date>2022-04-18</dc:date>
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