Large Language Models (LLMs) have gained prominence in various applications, including security. This paper explores the utility of LLMs in scam detection, a critical aspect of cybersecurity. Unlike traditional applications, we propose a novel use case for LLMs to identify scams, such as phishing, advance fee fraud, and romance scams. We present notable security applications of LLMs and discuss the unique challenges posed by scams. Specifically, we outline the key steps involved in building an effective scam detector using LLMs, emphasizing data collection, preprocessing, model selection, training, and integration into target systems. Additionally, we conduct a preliminary evaluation using GPT-3.5 and GPT-4 on a duplicated email, highlighting their proficiency in identifying common signs of phishing or scam emails. The results demonstrate the models' effectiveness in recognizing suspicious elements, but we emphasize the need for a comprehensive assessment across various language tasks. The paper concludes by underlining the importance of ongoing refinement and collaboration with cybersecurity experts to adapt to evolving threats.

翻译：大型语言模型（LLMs）已在包括安全领域在内的各类应用中崭露头角。本文探讨了LLMs在网络安全关键环节——诈骗检测中的效用。不同于传统应用，我们提出了一种新型用例：利用LLMs识别钓鱼诈骗、预付费诈骗和浪漫诈骗等欺诈行为。我们展现了LLMs在安全领域的显著应用，并论述了诈骗带来的独特挑战。具体而言，我们系统阐述了使用LLMs构建有效诈骗检测器的关键步骤，重点涵盖数据采集、预处理、模型选择、训练及与目标系统的集成。此外，我们基于重复邮件，使用GPT-3.5和GPT-4进行了初步评估，突出了它们在识别钓鱼或诈骗邮件常见特征方面的能力。结果表明这些模型在识别可疑要素方面具有有效性，但我们也强调需跨各类语言任务进行综合评估的必要性。本文最后指出，必须持续优化并与网络安全专家协作，以适应不断演变的威胁态势。