2026-09-19T03:48:06Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1515452023-08-01T03:51:26Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Hemberg, Erik
O’Reilly, Una-May
Liu, Kyle
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:47:35Z
2023-07-31T19:47:35Z
2023-06
2023-06-06T16:35:39.836Z
https://hdl.handle.net/1721.1/151545
Machine Learning techniques can provide insight in a variety of inference tasks involving not only text data but also source code. We apply these techniques to BRON, a graph database linking cybersecurity threats, vulnerability sources, and mitigation techniques, in order to extract a wider variety of relationships, and more effectively analyze them. We find that prompt engineering in large language models improves performance in edge classification within BRON. We in addition explore these inferences in practice, by modeling the interaction between cybersecurity attackers and defenders on a given network in a zero-sum game. We apply coevolution in a novel multi-step feedback framework to improve performance in modelling attacks, and find that allowing attackers to dynamically select their attack strategies improves their payoff.
M.Eng.
Massachusetts Institute of Technology
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Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Inference of Cyber Threats, Vulnerabilities, and Mitigations to Enhance Cybersecurity Simulations
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>Machine Learning techniques can provide insight in a variety of inference tasks involving not only text data but also source code. We apply these techniques to BRON, a graph database linking cybersecurity threats, vulnerability sources, and mitigation techniques, in order to extract a wider variety of relationships, and more effectively analyze them. We find that prompt engineering in large language models improves performance in edge classification within BRON. We in addition explore these inferences in practice, by modeling the interaction between cybersecurity attackers and defenders on a given network in a zero-sum game. We apply coevolution in a novel multi-step feedback framework to improve performance in modelling attacks, and find that allowing attackers to dynamically select their attack strategies improves their payoff.</Abstract>
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