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Enhancing Phishing Email Identification with Large Language Models

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arxiv 2502.04759 v1 pith:KL7BAXOQ submitted 2025-02-07 cs.CR cs.AI

classification cs.CRcs.AI
keywords phishingdetectingemailshighlanguagelargemodelsaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability

    cs.CR 2025-06 conditional novelty 5.0 of 10

    Fine-tuned LLMs for phishing detection show a dissociation between self-consistent explanations and classification accuracy, with Llama models scoring high on CC-SHAP but low on phishing detection while Wizard scores ...

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