Event Details
Adaptive Authorization Through Transformer-Based Tabular Learning
Presenter: Pratik Sinha
Supervisor:
Date: Thu, December 11, 2025
Time: 10:00:00 - 00:00:00
Place: Zoom - see below.
ABSTRACT
Meeting Details:
Time: Dec 11, 2025 10:00 AM Vancouver
Join Zoom Meeting
Meeting ID: 813 9937 6372
One tap mobile
+16475580588,,81399376372# Canada
+17789072071,,81399376372# Canada
Dial by your location
+1 647 558 0588 Canada
+1 778 907 2071 Canada
Meeting ID: 813 9937 6372
Find your local number:
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, attributes, 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 benchmarked 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.