Inference of Cyber Threats, Vulnerabilities, and Mitigations to Enhance Cybersecurity Simulations

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Liu, Kyle

Advisors dc:contributor.advisor

• Hemberg, Erik
• O’Reilly, Una-May

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151545

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151545