用大模型提升钓鱼邮件识别准确率，还能解释判断依据。
Enhancing Phishing Email Identification with Large Language Models
- 用大语言模型分析邮件内容，自动识别钓鱼特征。
- 检测准确率高，且在高精度下表现稳定。
- 结果可解释，适合安全团队快速验证邮件风险。
钓鱼攻击仍是当今数字世界中常见的网络犯罪手段，随着攻击方式日益复杂，亟需高效检测与防范方法。针对这一挑战，研究人员已提出多种基于机器学习的解决方案。本文研究大语言模型（LLM）在钓鱼邮件检测中的有效性。实验表明，该模型在保持高精度的同时实现了较高的准确率，并能提供决策的可解释证据，有助于增强检测系统的可信度与实用性。
原文摘要 · Abstract (English)
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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