Summary of Prompted Contextual Vectors For Spear-phishing Detection, by Daniel Nahmias et al.
Prompted Contextual Vectors for Spear-Phishing Detection
by Daniel Nahmias, Gal Engelberg, Dan Klein, Asaf Shabtai
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a detection approach to address the growing threat of large language models (LLMs) generating convincing spear-phishing emails. The authors utilize an ensemble of LLMs to create representation vectors based on novel document vectorization methods that quantify the presence of common persuasion principles in email content. This is achieved by prompting LLMs to reason and respond to human-crafted questions, producing prompted contextual document vectors for a downstream supervised machine learning model. The method is evaluated using a unique dataset generated by a proprietary system that automates target reconnaissance and spear-phishing email creation. The authors achieve a 91% F1 score in identifying LLM-generated spear-phishing emails, with the training set comprising only traditional phishing and benign emails. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep our online lives safe from sneaky hackers who use big language models to send fake emails that trick people into sharing secrets or clicking bad links. To fight back, researchers created a new way to analyze emails using these same language models. They taught the models to answer questions about what makes an email convincing or suspicious, and then used those answers to create special vectors that can be fed into machines learning algorithms. This method is super good at catching fake emails, even when they’re super sneaky! The researchers hope this will help protect people from these types of attacks. |
Keywords
* Artificial intelligence * F1 score * Machine learning * Prompting * Supervised * Vectorization
