LLM-Based Framework for Email Classification and Phishing Detection

Phishing and spam attacks continue to pose significant security risks due to their evolving nature, with traditional email classification methods often unable to effectively address these challenges. This paper proposes a framework that uses Large Language Models (LLMs) for enhanced email categorization and phishing detection. Through an inbox plugin, a user reports an email, which is then stored in a database, cleaned, and thoroughly analyzed using a series of specific prompts determining a classification of "normal", "unwanted", or "malicious" email.

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Publication Details

Journal
ACM SIGAda Ada Letters
Published
2025-06-01
DOI
https://doi.org/10.1145/3784987.3784993
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

LLM-Based Framework for Email Classification and Phishing Detection

José Cecílio, Dulce Domingos, Mónica Rosa, Nuno Costa et al.
ACM SIGAda Ada Letters
Spam and Phishing Detection
article

LLM-Based Framework for Email Classification and Phishing Detection

José Cecílio, Dulce Domingos, Mónica Rosa, Nuno Costa, Ruben Salgueiro
article en

Abstract

Phishing and spam attacks continue to pose significant security risks due to their evolving nature, with traditional email classification methods often unable to effectively address these challenges. This paper proposes a framework that uses Large Language Models (LLMs) for enhanced email categorization and phishing detection. Through an inbox plugin, a user reports an email, which is then stored in a database, cleaned, and thoroughly analyzed using a series of specific prompts determining a classification of "normal", "unwanted", or "malicious" email.

ACM SIGAda Ada LettersVol. 45(1)
University of Lisbon (PT), Universidade Europeia (PT)
Openalex Percentile: Top 20%
Spam and Phishing Detection
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