Phishing email is a serious cyber threat that tries to deceive users by sending false emails with the intention of stealing confidential information or causing financial harm. Attackers, often posing as trustworthy entities, exploit technological advancements and sophistication to make detection and prevention of phishing more challenging. Despite extensive academic research, phishing detection remains an ongoing and formidable challenge in the cybersecurity landscape. Large Language Models (LLMs) and Masked Language Models (MLMs) possess immense potential to offer innovative solutions to address long-standing challenges. In this research paper, we present an optimized, fine-tuned transformer-based DistilBERT model designed for the detection of phishing emails. In the detection process, we work with a phishing email dataset and utilize the preprocessing techniques to clean and solve the imbalance class issues. Through our experiments, we found that our model effectively achieves high accuracy, demonstrating its capability to perform well. Finally, we demonstrate our fine-tuned model using Explainable-AI (XAI) techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Transformer Interpret to explain how our model makes predictions in the context of text classification for phishing emails.

翻译：钓鱼邮件是一种严重的网络威胁，其通过发送虚假邮件试图窃取机密信息或造成经济损失。攻击者常伪装成可信实体，利用技术进步和复杂手段使得检测和防范钓鱼攻击更具挑战性。尽管已有大量学术研究，但钓鱼检测仍是网络安全领域长期存在的重大难题。大语言模型（LLMs）和遮蔽语言模型（MLMs）为解决这一长期挑战提供了极具潜力的创新方案。本研究提出一种经优化微调的基于Transformer的DistilBERT模型，专用于钓鱼邮件检测。在检测过程中，我们使用钓鱼邮件数据集，并采用预处理技术解决数据清洗和类别不平衡问题。实验表明，该模型有效实现了高准确率，展现了卓越性能。最后，我们运用可解释人工智能（XAI）技术（如局部可解释模型无关解释LIME和Transformer Interpret）展示微调模型，阐明模型在钓鱼邮件文本分类任务中的预测机制。