Abstract
Cybersecurity is a technological focus of individuals, businesses, and governments due to increasing threats, the sophistication of attacks, and the growing number of smart devices. Planning, assessment, and training in cybersecurity operations have also grown to combat these threats, resulting in a boom in cyber defense software and services, workforce development and career opportunities, and research in automated cyber technologies. However, building and maintaining a new workforce and developing innovative cyber-threat solutions are expensive and time-consuming. This thesis introduces a configurable machine-learning environment tailored for training agents that uses different reinforcement learning algorithms within the cybersecurity domain. The environment allows agents to learn simulated cyber-attacks, which act as opposition forces in a realistic, controlled setting that reduces the risk to real computer networks. The thesis also investigates relevant research on machine learning agents for cybersecurity, discusses the simulation architecture, and describes experiments utilizing the Proximal Policy Optimization and Advantage Actor-Critic algorithms. The objective of the thesis is to determine the superior algorithm for automatically identifying exploitable vulnerabilities by evaluating the performance based on accuracy, detected vulnerabilities, and time efficiency.
Notes
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.
Graduation Date
2023
Semester
Summer
Advisor
Mondesire, Sean
Degree
Master of Science (M.S.)
College
College of Engineering and Computer Science
Department
School of Modeling, Simulation, and Training
Degree Program
Modeling & Simulation
Identifier
CFE0009891; DP0028424
URL
https://purls.library.ucf.edu/go/DP0028424
Language
English
Release Date
February 2025
Length of Campus-only Access
1 year
Access Status
Masters Thesis (Campus-only Access)
STARS Citation
Müller, Daniel, "Deep Reinforcement Learning for Automated Cybersecurity Threat Detection" (2023). Electronic Theses and Dissertations, 2020-2023. 1920.
https://stars.library.ucf.edu/etd2020/1920
Restricted to the UCF community until February 2025; it will then be open access.