Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Ching-Seh Wu

Second Advisor

Navrati Saxena

Third Advisor

Thomas Austin

Keywords

Adaptive Phishing Detection, Continual Learning, Elastic Weight Consolidation, Learning Without Forgetting, Large Language Models, GPT-4o-mini

Abstract

Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior outputs when adapting to new data. On the other hand, GPT-4o-mini is evaluated through zero-shot prompting for classification and also used to generate synthetic phishing emails, addressing data scarcity and imbalance. A prototype Chrome extension is developed to demonstrate the usefulness of our adaptive system in real-time email filtering. In our experiments on four sequential phishing datasets from different time periods, RoBERTa + EWC preserved accuracies of 93%, 84%, 87% and 98% respectively, dramatically reducing catastrophic forgetting seen with static fine-tuning. RoBERTa + LWF delivered even better results, achieving accuracies of 96%, 85%, 97% and 99% on the same splits.

Download

Available for download on Monday, May 25, 2026

Share

COinS