Analysis and prevention of AI-based phishing email attacks
Chibuike Samuel Eze, Lior Shamir

TL;DR
This paper presents a study on AI-generated phishing emails, demonstrating that machine learning can effectively distinguish them from human-crafted scams, and introduces a public corpus for further research.
Contribution
It provides a new corpus of AI-generated phishing emails and evaluates machine learning methods for detecting these sophisticated attacks.
Findings
Machine learning tools can identify AI-generated phishing emails with high accuracy.
AI-generated emails differ stylistically from human-crafted phishing emails.
Training detection systems with AI-generated emails is crucial for future cybersecurity defenses.
Abstract
Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam email. By applying…
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Taxonomy
TopicsSpam and Phishing Detection
