Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection
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| Format: | Preprint |
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2025
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| author | Adjei, Yaw Osei Ayivor, Frederick |
| author_facet | Adjei, Yaw Osei Ayivor, Frederick |
| contents | Business Email Compromise (BEC) is a high-impact social engineering threat with extreme operational asymmetry: false negatives can trigger large financial losses, while false positives primarily incur investigation and delay costs. This paper compares two BEC detection paradigms under a cost-sensitive decision framework: (i) a semantic transformer approach (DistilBERT) for contextual language understanding, and (ii) a forensic psycholinguistic approach (CatBoost) using engineered linguistic and structural cues. We evaluate both on a hybrid dataset (N = 7,990) combining legitimate corporate email and AI-synthesised adversarial fraud generated across 30 BEC taxonomies, including character-level Unicode obfuscations. We add classical baselines (TF-IDF+LogReg and character n-gram+Linear SVM), an ablation study for the Smiling Assassin Score, and a homoglyph-map sensitivity analysis. DistilBERT achieves AUC = 1.0000 and F1 = 0.9981 at 7.403 ms per email on GPU; CatBoost achieves AUC = 0.9860 and F1 = 0.9382 at 0.855 ms on CPU. A three-way cost-sensitive decision policy (auto-allow, auto-block, manual review) optimises expected financial loss under a 1:5,167 false-negative-to-false-positive cost ratio. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_20944 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection Adjei, Yaw Osei Ayivor, Frederick Machine Learning Cryptography and Security K.6.5; I.2.7; I.5.1 Business Email Compromise (BEC) is a high-impact social engineering threat with extreme operational asymmetry: false negatives can trigger large financial losses, while false positives primarily incur investigation and delay costs. This paper compares two BEC detection paradigms under a cost-sensitive decision framework: (i) a semantic transformer approach (DistilBERT) for contextual language understanding, and (ii) a forensic psycholinguistic approach (CatBoost) using engineered linguistic and structural cues. We evaluate both on a hybrid dataset (N = 7,990) combining legitimate corporate email and AI-synthesised adversarial fraud generated across 30 BEC taxonomies, including character-level Unicode obfuscations. We add classical baselines (TF-IDF+LogReg and character n-gram+Linear SVM), an ablation study for the Smiling Assassin Score, and a homoglyph-map sensitivity analysis. DistilBERT achieves AUC = 1.0000 and F1 = 0.9981 at 7.403 ms per email on GPU; CatBoost achieves AUC = 0.9860 and F1 = 0.9382 at 0.855 ms on CPU. A three-way cost-sensitive decision policy (auto-allow, auto-block, manual review) optimises expected financial loss under a 1:5,167 false-negative-to-false-positive cost ratio. |
| title | Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection |
| topic | Machine Learning Cryptography and Security K.6.5; I.2.7; I.5.1 |
| url | https://arxiv.org/abs/2511.20944 |