Inference of Cyber Threats, Vulnerabilities, and Mitigations to Enhance Cybersecurity Simulations
Name
liu-kyleliu-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
1.94 MB
Format
Adobe PDF
Checksum (MD5)
6f0587c1e472a5c5107c24d112c429c3
Author(s)
Liu, Kyle
Advisor(s)
Hemberg, Erik
O’Reilly, Una-May
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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