{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/143257"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/143257","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Using Machine Learning for Description and Inference of Cyber Threats, Vulnerabilities, and Mitigations","abstract":"Machine learning and natural language processing (NLP) can help describe and make inferences on the vast amount of text data in cybersecurity. We use a graph database named BRON, which contains data from publicly available threat and vulnerability sources, for machine learning inference. Applying machine learning to BRON can provide us with more robust relationships, which can improve defenses against cyber threats. We experiment with different feature representations and subsets of the data, and show that machine learning and NLP can effectively classify edges between entries from different data sources as well as predict possible edge candidates. Experts agree that several of our predicted candidates are plausible edges. We also analyze defensive mitigation similarities using NLP techniques and find that there are identical mitigation descriptions for some entries that have internal relationships.","abstract_html":"Machine learning and natural language processing (NLP) can help describe and make inferences on the vast amount of text data in cybersecurity. We use a graph database named BRON, which contains data from publicly available threat and vulnerability sources, for machine learning inference. Applying machine learning to BRON can provide us with more robust relationships, which can improve defenses against cyber threats. We experiment with different feature representations and subsets of the data, and show that machine learning and NLP can effectively classify edges between entries from different data sources as well as predict possible edge candidates. Experts agree that several of our predicted candidates are plausible edges. We also analyze defensive mitigation similarities using NLP techniques and find that there are identical mitigation descriptions for some entries that have internal relationships.","abstract_has_math":false,"creators":["Srinivasan, Ashwin"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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We use a graph database named BRON, which contains data from publicly available threat and vulnerability sources, for machine learning inference. Applying machine learning to BRON can provide us with more robust relationships, which can improve defenses against cyber threats. We experiment with different feature representations and subsets of the data, and show that machine learning and NLP can effectively classify edges between entries from different data sources as well as predict possible edge candidates. Experts agree that several of our predicted candidates are plausible edges. We also analyze defensive mitigation similarities using NLP techniques and find that there are identical mitigation descriptions for some entries that have internal relationships."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Using Machine Learning for Description and Inference of Cyber Threats, Vulnerabilities, and Mitigations"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hemberg, Erik","O’Reilly, Una-May"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Srinivasan, Ashwin"],"dc:date.accessioned":["2022-06-15T13:07:45Z"],"dc:date.available":["2022-06-15T13:07:45Z"],"dc:date.issued":["2022-02"],"dc:description.abstract":["Machine learning and natural language processing (NLP) can help describe and make inferences on the vast amount of text data in cybersecurity. We use a graph database named BRON, which contains data from publicly available threat and vulnerability sources, for machine learning inference. Applying machine learning to BRON can provide us with more robust relationships, which can improve defenses against cyber threats. We experiment with different feature representations and subsets of the data, and show that machine learning and NLP can effectively classify edges between entries from different data sources as well as predict possible edge candidates. Experts agree that several of our predicted candidates are plausible edges. We also analyze defensive mitigation similarities using NLP techniques and find that there are identical mitigation descriptions for some entries that have internal relationships."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/143257"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Using Machine Learning for Description and Inference of Cyber Threats, Vulnerabilities, and Mitigations"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:20:47Z"}