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<p>Email-based attacks, such as Business Email Compromise, seriously threaten many organizations. In recent years, Large Language Models have improved the potency of email-based attacks by giving attackers an easy-to-use tool to overcome the language barrier and craft believable emails. At the same time, Business Email Compromise research remains hamstrung by the lack of a publicly available dataset. This paper proposes a novel system composed of Large Language Models to create Business Email Compromise datasets. Two datasets are generated. The first one (BEC-1) is a small 20-email proof-of-concept dataset that demonstrates that the system produces a dataset that a human analyst finds credible. The second (BEC-2) is a larger 279-email dataset generated using the same system. BEC-2 is the first public Business Email Compromise dataset available to the email security research community. The paper also proposes an accuracy-like metric called “agreement score” to measure the quality of datasets produced. Both BEC-1 and BEC-2 have high agreement scores – 90 and 93, respectively – validating the effectiveness of the Large Language Model system.</p>

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Building a business email compromise research dataset with large language models

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Rohit Dube

摘要

Email-based attacks, such as Business Email Compromise, seriously threaten many organizations. In recent years, Large Language Models have improved the potency of email-based attacks by giving attackers an easy-to-use tool to overcome the language barrier and craft believable emails. At the same time, Business Email Compromise research remains hamstrung by the lack of a publicly available dataset. This paper proposes a novel system composed of Large Language Models to create Business Email Compromise datasets. Two datasets are generated. The first one (BEC-1) is a small 20-email proof-of-concept dataset that demonstrates that the system produces a dataset that a human analyst finds credible. The second (BEC-2) is a larger 279-email dataset generated using the same system. BEC-2 is the first public Business Email Compromise dataset available to the email security research community. The paper also proposes an accuracy-like metric called “agreement score” to measure the quality of datasets produced. Both BEC-1 and BEC-2 have high agreement scores – 90 and 93, respectively – validating the effectiveness of the Large Language Model system.