Enhancing Phishing Email Identification with Large Language Models
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917916199354368 |
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| author | Lee, Catherine |
| author_facet | Lee, Catherine |
| contents | Phishing has long been a common tactic used by cybercriminals and continues to pose a significant threat in today's digital world. When phishing attacks become more advanced and sophisticated, there is an increasing need for effective methods to detect and prevent them. To address the challenging problem of detecting phishing emails, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. In this work, we take steps to study the efficacy of large language models (LLMs) in detecting phishing emails. The experiments show that the LLM achieves a high accuracy rate at high precision; importantly, it also provides interpretable evidence for the decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Phishing Email Identification with Large Language Models Lee, Catherine Cryptography and Security Artificial Intelligence Phishing has long been a common tactic used by cybercriminals and continues to pose a significant threat in today's digital world. When phishing attacks become more advanced and sophisticated, there is an increasing need for effective methods to detect and prevent them. To address the challenging problem of detecting phishing emails, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. In this work, we take steps to study the efficacy of large language models (LLMs) in detecting phishing emails. The experiments show that the LLM achieves a high accuracy rate at high precision; importantly, it also provides interpretable evidence for the decisions. |
| title | Enhancing Phishing Email Identification with Large Language Models |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2502.04759 |