Term of Award
Fall 2023
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
Stephen Carden
Committee Member 2
Kyle Bradford
Abstract
This thesis delves into cybersecurity by applying Deep Reinforcement(DRL) Learning in network intrusion detection. One advantage of DRL is the ability to adapt to changing network conditions and evolving attack methods, making it a promising solution for addressing the challenges involved in intrusion detection. The thesis will also discuss the obstacles and benefits of using Classification methods for network intrusion detection and the need for high-quality training data. To train and test our proposed method, the NSL-KDD dataset was used and then adjusted by converting it from a multi-classification to a binary classification, achieved by joining all attacks into one. This approach sets apart our research from other studies.
Recommended Citation
Sanusi, Hamed T., "Network Intrusion Detection Using Deep Reinforcement Learning" (2023). Electronic Theses and Dissertations. 2676.
https://digitalcommons.georgiasouthern.edu/etd/2676
Research Data and Supplementary Material
Yes
Included in
Applied Statistics Commons, Data Science Commons, Other Statistics and Probability Commons, Statistical Models Commons
