PEEK: Phishing Evolution Framework for Phishing Generation and Evolving Pattern Analysis using Large Language Models
Fengchao Chen, Tingmin Wu, Van Nguyen, Shuo Wang, Alsharif Abuadbba,, Carsten Rudolph

TL;DR
PEEK is a framework that uses large language models to generate diverse, high-quality phishing datasets and analyze evolving attack patterns, significantly improving detection robustness and reducing adversarial vulnerability.
Contribution
This paper introduces PEEK, the first framework that augments phishing datasets with LLMs and analyzes pattern evolution to enhance detection of sophisticated phishing attacks.
Findings
Increases usable phishing samples from 21.4% to 84.8%.
Improves detection accuracy to over 88%.
Reduces adversarial sensitivity by up to 70%.
Abstract
Phishing remains a pervasive cyber threat, as attackers craft deceptive emails to lure victims into revealing sensitive information. While Artificial Intelligence (AI), in particular, deep learning, has become a key component in defending against phishing attacks, these approaches face critical limitations. The scarcity of publicly available, diverse, and updated data, largely due to privacy concerns, constrains detection effectiveness. As phishing tactics evolve rapidly, models trained on limited, outdated data struggle to detect new, sophisticated deception strategies, leaving systems and people vulnerable to an ever-growing array of attacks. We propose the first Phishing Evolution FramEworK (PEEK) for augmenting phishing email datasets with respect to quality and diversity, and analyzing changing phishing patterns for detection to adapt to updated phishing attacks. Specifically, we…
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Taxonomy
TopicsSpam and Phishing Detection · Misinformation and Its Impacts · Topic Modeling
