用于鱼叉式网络钓鱼检测的提示上下文向量
Prompted Contextual Vectors for Spear-Phishing Detection
AI总结：
提出基于LLM推理的文档向量化方法，通过量化邮件中的说服原则生成上下文向量，在仅用传统邮件训练下，对LLM生成的鱼叉式钓鱼邮件检测F1达91%。
摘要：
鱼叉式网络钓鱼攻击构成了重大的安全挑战，大型语言模型（LLM）通过生成逼真的电子邮件和协助目标侦察加剧了这一威胁。为了解决这个问题，我们提出了一种基于新型文档向量化方法的检测方法，该方法利用 LLM 集成来创建表示向量。通过提示 LLM 推理并回答人工设计的问题，我们量化了电子邮件内容中常见说服原则的存在情况，为下游监督机器学习模型生成提示上下文文档向量。我们使用由专有系统生成的独特数据集评估了我们的方法，该系统可自动进行目标侦察和鱼叉式网络钓鱼电子邮件创建。我们的方法在识别 LLM 生成的鱼叉式网络钓鱼电子邮件方面达到了 91% 的 F1 分数，而训练集仅包含传统的网络钓鱼和良性电子邮件。主要贡献包括一种利用 LLM 推理的新型文档向量化方法、一个公开的高质量鱼叉式网络钓鱼电子邮件数据集，以及我们的方法在检测此类电子邮件方面所证明的有效性。该方法可用于各种文档分类任务，特别是在对抗性问题领域。
英文摘要：
Spear-phishing attacks present a significant security challenge, with large language models (LLMs) escalating the threat by generating convincing emails and facilitating target reconnaissance. To address this, we propose a detection approach based on a novel document vectorization method that utilizes an ensemble of LLMs to create representation vectors. By prompting LLMs to reason and respond to human-crafted questions, we quantify the presence of common persuasion principles in the email's content, producing prompted contextual document vectors for a downstream supervised machine learning model. We evaluate our method using a unique dataset generated by a proprietary system that automates target reconnaissance and spear-phishing email creation. Our method achieves a 91\% F1 score in identifying LLM-generated spear-phishing emails, with the training set comprising only traditional phishing and benign emails. Key contributions include a novel document vectorization method utilizing LLM reasoning, a publicly available dataset of high-quality spear-phishing emails, and the demonstrated effectiveness of our method in detecting such emails. This methodology can be utilized for various document classification tasks, particularly in adversarial problem domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
