Detecting AI-Generated Phishing and Social Engineering Attacks Using  Machine Learning Classifiers.

Nidhi Srivastava

Detecting AI-Generated Phishing and Social Engineering Attacks Using Machine Learning Classifiers.

Authors

Nidhi Srivastava

Keywords:

AI-Generated Phishing, Social Engineering, Machine Learning, Large Language Models, URL Analysis, Random Forest, XGBoost, Explainability

Abstract

Large language models have made it inexpensive to produce fluent, personalised and grammaticallyflawless phishing messages, removing many of the surface cues that users and legacy filters relied upon. Thispaper studies how supervised machine learning classifiers can detect AI-generated phishing and related socialengineering attacks. We characterise the threat, propose a detection framework

References

1: Pawan Kumar Singh, Scaling Gate-All-Around (GAA) Transistor Architectures for Sub-2nm AI Accelerators Using Atomically Thin 2D Materials, Vol. 33 No. 08 (2024): JOCAAA, Journal Name: Journal of Computational Analysis and Applications, Journal's ISSN: 1521-1398 (Paper),1572-9206

(Online), https://eudoxuspress.com/index.php/pub/article/view/5713

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Published

2025-05-20

How to Cite

Nidhi Srivastava. (2025). Detecting AI-Generated Phishing and Social Engineering Attacks Using Machine Learning Classifiers . Journal of Computational Analysis and Applications (JoCAAA), 34(5), 528–542. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5870