mlsanityData to 7 Oct 2026

Analysis and prevention of AI-based phishing email attacks

arXiv 2405.05435 ↗First posted 8 May 2024cs.CR cs.AI cs.CLcited by 6

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Abstract (arXiv)

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 descriptive analytic, the specific differences between AI-generated emails and manually crafted scam emails are profiled, and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that is made open to the public, and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing email is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 58 references that are in the map. First posted on arXiv 8 May 2024.

Builds on

7 of 58 references.

References · 7

  1. 2023GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Modelscited by 40
  2. 2021Copyright in Generative Deep Learningcited by 29
  3. 2016Breaching the Human Firewall: Social engineering in Phishing and Spear-Phishing Emailscited by 7
  4. 2020High Accuracy Phishing Detection Based on Convolutional Neural Networkscited by 7
  5. 2023A data science and machine learning approach to continuous analysis of Shakespeare's playscited by 2
  6. 2014Network Analysis with the Enron Email Corpuscited by 1
  7. 2024Why People Still Fall for Phishing Emails: An Empirical Investigation into How Users Make Email Response Decisionscited by 1

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