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Topic

Adaptive Authorization through Transformer-Based Learning

Department of Electrical and Computer Engineering

Date & location

• Monday, December 15, 2025

• 1:00 P.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. Navneet Kaur Popli, Department of Electrical and Computer Engineering, ӣ��Ӱ�� (Co-Supervisor)

• Dr. Stephen Neville, Department of Electrical and Computer Engineering, UVic (Co-Supervisor) 

External Examiner

• Dr. Alex Thomo, Department of Computer Science, ӣ��Ӱ�� 

Chair of Oral Examination

• Dr. Julian Lum, Department of Biochemistry and Microbiology, UVic

   

Abstract

Access control is a cornerstone of information security, defining how entities interact with protected digital resources. Traditional rule-based frameworks, though effective in static environments, struggle to adapt to modern, data-intensive ecosystems where roles, at tributes, and contextual conditions evolve continuously. Recent advances in machine learning have introduced new opportunities to automate access control through predictive and adaptive modeling yet progress remains constrained by the scarcity of real-world datasets, inconsistent benchmarking methodologies, and limited evaluation under controlled data conditions. This thesis presents a reproducible framework for evaluating machine-learning based access control models using synthetic, configurable datasets. The proposed data generation process emulates healthcare authorization structures, incorporating tunable role hierarchies, permission ratios, and anomaly patterns to simulate varying entropy and data noise. A suite of ML architectures, including decision-tree ensembles, feed-forward networks, residual networks, and transformer-based tabular models are systematically bench marked using standardized preprocessing and evaluation metrics. Experimental results reveal that decision-tree ensembles achieve strong baseline performance on small, structured datasets, while neural and transformer-based models exhibit greater robustness, scalability, and generalization as dataset volume and entropy increase. These findings validate the effectiveness of synthetic datasets for reproducible access-control research and demonstrate the impact of data scale on model elasticity and stability. Collectively, this work advances the development of reliable, ML-driven authorization systems through a trans parent methodology for benchmarking and comparative analysis.