Phishing and spam detection is long standing challenge that has been the
subject of much academic research. Large Language Models (LLM) have vast
potential to transform society and provide new and innovative approaches to
solve well-established challenges. Phishing and spam have caused financial
hardships and lost time and resources to email users all over the world and
frequently serve as an entry point for ransomware threat actors. While
detection approaches exist, especially heuristic-based approaches, LLMs offer
the potential to venture into a new unexplored area for understanding and
solving this challenge. LLMs have rapidly altered the landscape from business,
consumers, and throughout academia and demonstrate transformational potential
for the potential of society. Based on this, applying these new and innovative
approaches to email detection is a rational next step in academic research. In
this work, we present IPSDM, our model based on fine-tuning the BERT family of
models to specifically detect phishing and spam email. We demonstrate our
fine-tuned version, IPSDM, is able to better classify emails in both unbalanced
and balanced datasets. This work serves as an important first step towards
employing LLMs to improve the security of our information systems.

利用基于 BERT 模型的 IPSDM 模型，本文首次尝试将大型语言模型应用于检测钓鱼邮件和垃圾邮件，取得了更好的分类效果，并为改善信息系统安全迈出重要的第一步。