AI-Powered Phishing Detection

This paper, titled “AI-Powered Phishing Detection,” proposes a machine-learning-based system for identifying potentially suspicious emails, URLs, and messages. The system aims to help users recognize phishing indicators, assess risk, and make safer decisions when handling digital communications. The proposed framework includes input validation, feature extraction, machine-learning classification, risk scoring, explanation of warning indicators, and a user-facing warning or review mechanism. Potential features include URL structure, hostname patterns, unusual symbols, message wording, sender metadata, and selected email-header information where reliable data are available. Candidate classification methods include Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. The paper describes dataset preparation, model training, validation, threshold selection, and evaluation using precision, recall, F1-score, false-positive rate, ROC-AUC or precision-recall AUC, response latency, and resource usage. The proposed system also emphasizes privacy protection, safe handling of suspicious content, human review, feedback collection, and monitoring for changes in phishing patterns. It is intended as a defensive screening aid that complements established cybersecurity controls rather than replacing them. This paper presents a proposed system architecture and evaluation plan. It does not claim experimentally measured detection accuracy. The scores shown in the paper are illustrative examples for interface design, not actual experimental results. Future work includes implementation, testing with an authorized and representative dataset, and evaluation against evolving phishing threats.