Term of Award
Spring 2025
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Zheni Utic
Committee Member 1
Ionut Iacob
Committee Member 2
Divine Wanduku
Abstract
Intrusion Detection Systems (IDS) play a crucial role in computer network security by identifying malicious activities and potential cyberattacks. This thesis combines machine learning and cybersecurity by applying Reinforcement Learning (RL) in intrusion detection and response using the NSL-KDD dataset.
We designed and implemented a Q-learning framework where an agent learns to classify network traffic over time by interacting with the environment and receiving rewards based on detection accuracy. We also look at the importance of feature selection and classification techniques and how effective they are in improving model performance, reducing the complexity of computation, and producing more desirable results.
This study highlights the potential of reinforcement learning, particularly Q-learning as a cutting-edge approach to improving modern intrusion detection & intrusion response systems.
Recommended Citation
Oyemaja, Ayomide, "Machine Learning Methods for Intrusion Detection and Response in Network Security" (2025). Electronic Theses and Dissertations. 2941.
https://digitalcommons.georgiasouthern.edu/etd/2941
Research Data and Supplementary Material
No
Included in
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, Cybersecurity Commons, Data Science Commons, Mathematics Commons
