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Identification and Classification of AI-Generated Phishing Campaigns Using Deep Seek-Driven Semantic Content Analysis

Orji, Cyrus Ebere, MCPN, Prof. Paul Nosike

Abstract

The advent of Large Language Models has established a new paradigm in cyber deception, allowing attackers to craft very convincing and personalized phishing content at scale. In this work, we propose a complete methodology for detecting and characterizing AI- generated phishing attacks through semantic text analysis based on DeepSeek. We propose a hybrid detection architecture, in which we combine the natural language understanding capabilities of DeepSeek for the semantic feature extraction with machine learning classification for detection of AI generated phishing emails. Our technique overcomes significant limitations of previous detection mechanisms, including the inability to recognize paraphrased text and the lack of interpretability in black-box detection models. We show that DeepSeek-based semantic analysis is better at identifying AI-generated phishing from legitimate messages, using a comprehensive investigation of email content aspects including semantic coherence, stylometric patterns, and psychological manipulation cues. We experimentally validated our approach on a bespoke Streamlit app that uses real DeepSeek API calls. Our approach, validated on 126 emails (63 phishing, 63 legitimate) using 5-fold cross-validation, achieved a detection accuracy of 96.2% when combining DeepSeek semantic analysis with the full 60-feature stylometric framework. The cross-validated accuracy on our dataset was 92.86% ± 2.1% with an F1-score of 0.928. The methodology could detect sophisticated phishing attacks that employed psychological manipulation tactics such as authority, urgency, scarcity, and social proof. The explainability layer generates natural language explanations that increase user confidence and provide actionable threat intelligence. Our results help to design adaptable and scalable defenses to the growing threat of AI-enabled phishing attempts, with implications for organizational security, regulatory compliance and cybersecurity awareness training.

Keywords

AI-generated phishingDeepSeeksemantic analysisphishing detectionLLM

References

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