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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.22515 |
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| _version_ | 1866913916713435136 |
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| author | Dalmiere, Antony Auriol, Guillaume Nicomette, Vincent Marchand, Pascal |
| author_facet | Dalmiere, Antony Auriol, Guillaume Nicomette, Vincent Marchand, Pascal |
| contents | Traditional phishing detection often overlooks psychological manipulation. This study investigates using Large Language Model (LLM) In-Context Learning (ICL) for fine-grained classification of phishing emails based on a taxonomy of 40 manipulation techniques. Using few-shot examples with GPT-4o-mini on real-world French phishing emails (SignalSpam), we evaluated performance against a human-annotated test set (100 emails). The approach effectively identifies prevalent techniques (e.g., Baiting, Curiosity Appeal, Request For Minor Favor) with a promising accuracy of 0.76. This work demonstrates ICL's potential for nuanced phishing analysis and provides insights into attacker strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-context learning for the classification of manipulation techniques in phishing emails Dalmiere, Antony Auriol, Guillaume Nicomette, Vincent Marchand, Pascal Cryptography and Security Artificial Intelligence Traditional phishing detection often overlooks psychological manipulation. This study investigates using Large Language Model (LLM) In-Context Learning (ICL) for fine-grained classification of phishing emails based on a taxonomy of 40 manipulation techniques. Using few-shot examples with GPT-4o-mini on real-world French phishing emails (SignalSpam), we evaluated performance against a human-annotated test set (100 emails). The approach effectively identifies prevalent techniques (e.g., Baiting, Curiosity Appeal, Request For Minor Favor) with a promising accuracy of 0.76. This work demonstrates ICL's potential for nuanced phishing analysis and provides insights into attacker strategies. |
| title | In-context learning for the classification of manipulation techniques in phishing emails |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2506.22515 |