Enhancing Network Security: Dynamical Intrusion Detection Systems leveraging Zero Trust Architecture
Date of Award
9-1-2025
Degree Type
Thesis
Degree Name
Master of Science (M.S.)
Department
Electrical & Computer Engineering
First Advisor
Kamrul Hasan
Abstract
This thesis discussed two original methodologies for proposing security frameworks incorporating machine learning (ML) and Zero Trust Architecture (ZTA) principles to manage advanced persistent threats faced by Unmanned Aerial Vehicles (UAVs) and Network intrusion detection systems (IDS). The first methodology examined the use of RF signals and deep learning to identify and classify UAVs. The RF signal characteristics used in the method for detecting UAVs improved the ability to determine RF drone protocols. Although the models led to promising findings, their lack of ability to generalize to new drone types identified the need to improve both the data set and the addition of anomaly detection to further the robustness. The second approach provided a model based on Proximal Policy Optimization (PPO) using reinforcement learning, a base framework in a zonal-based ZTA to develop a dynamic IDS. The model operated in an implementation tested against the CIC-IDS2017 data, establishing an improved detection of advanced cyber threats. The model also included explainable AI components, such as SHAP and LIME, which were important in improving accountability and transparency by elucidating the rationales of model decisions. The results of both methods illustrated a significant increase in accuracy, adaptability, and trust. Future work is planned to modify UAV datasets and add least-privileged access applied in conjunction with micro-segmentation to further develop the IDS framework.
Recommended Citation
Haque, Ekramul, "Enhancing Network Security: Dynamical Intrusion Detection Systems leveraging Zero Trust Architecture" (2025). Tennessee State University Alumni Theses and Dissertations. 274.
https://digitalscholarship.tnstate.edu/alumni-etd/274