Adapting to Cyber Threats: A Phishing Evolution Network (PEN) Framework for Phishing Generation and Analyzing Evolution Patterns using Large Language Models

• URL: http://arxiv.org/abs/2411.11389v1
• Date: Mon, 18 Nov 2024 09:03:51 GMT
• Title: Adapting to Cyber Threats: A Phishing Evolution Network (PEN) Framework for Phishing Generation and Analyzing Evolution Patterns using Large Language Models
• Authors: Fengchao Chen, Tingmin Wu, Van Nguyen, Shuo Wang, Hongsheng Hu, Alsharif Abuadbba, Carsten Rudolph,
• Abstract summary: Phishing remains a pervasive cyber threat, as attackers craft deceptive emails to lure victims into revealing sensitive information. While Artificial Intelligence (AI) has become a key component in defending against phishing attacks, these approaches face critical limitations. We propose the Phishing Evolution Network (PEN), a framework leveraging large language models (LLMs) and adversarial training mechanisms to continuously generate high quality and realistic diverse phishing samples.
• Score: 10.58220151364159
• License:
• Abstract: Phishing remains a pervasive cyber threat, as attackers craft deceptive emails to lure victims into revealing sensitive information. While Artificial Intelligence (AI), particularly 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 their effectiveness. As phishing tactics evolve rapidly, models trained on limited, outdated data struggle to detect new, sophisticated deception strategies, leaving systems vulnerable to an ever-growing array of attacks. Addressing this gap is essential to strengthening defenses in an increasingly hostile cyber landscape. To address this gap, we propose the Phishing Evolution Network (PEN), a framework leveraging large language models (LLMs) and adversarial training mechanisms to continuously generate high quality and realistic diverse phishing samples, and analyze features of LLM-provided phishing to understand evolving phishing patterns. We evaluate the quality and diversity of phishing samples generated by PEN and find that it produces over 80% realistic phishing samples, effectively expanding phishing datasets across seven dominant types. These PEN-generated samples enhance the performance of current phishing detectors, leading to a 40% improvement in detection accuracy. Additionally, the use of PEN significantly boosts model robustness, reducing detectors' sensitivity to perturbations by up to 60%, thereby decreasing attack success rates under adversarial conditions. When we analyze the phishing patterns that are used in LLM-generated phishing, the cognitive complexity and the tone of time limitation are detected with statistically significant differences compared with existing phishing.

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