Prompted Contextual Vectors for Spear-Phishing Detection
CoRR(2024)
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
emails, with the training set comprising only traditional phishing and benign
emails. Key contributions include an innovative 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要