AI Phishing Email Detector: A Review of Transformer-Based NLP, Sender and Url Analysis, and Defence Against AI-Generated Phishing

Phishing emails deceive individuals into revealing credentials and personal data, and attackers can now use large language models to write fluent, personalized phishing messages at scale that slip past rule-based filters. This paper reviews AI techniques for an adaptive phishing email detector that analyses message text, sender information and embedded URLs in real time. It traces detection from structural and feature-based approaches through natural-language analysis of intent and urgency to transformer models such as BERT and RoBERTa fine-tuned for phishing classification. URL analysis with deep character models, sender-authentication checks and behavioural signals are reviewed. Particular attention is given to the new threat of AI-generated phishing, including studies showing that language models can craft convincing spear-phishing emails and that the same models can help detect them. A five-layer architecture comprising email ingestion, multi-view feature extraction, transformer-based and URL classifiers, risk fusion with explanation, and user interaction with feedback is proposed. Research gaps, objectives, a comparison of models and challenges including adversarial adaptation, false positives, privacy and multilingual phishing are discussed. The review concludes that adaptive, explainable, multi-view detection is necessary as phishing becomes AI-assisted.