Predicting Emerging Phishing Threats Through Lightweight AI Data Mining on the Edge

Abstract

Phishing remains a cybersecurity risk by exploiting human nature and technology loopholes to steal sensitive data. In this research, with secondary qualitative analysis including academic databases and content analysis, machine-learning-based phishing models with multidimensional feature analysis are critically analyzed. Computational inefficiency, real-time detection capability, and ethics concerns like model explainability and privacy are some of the challenges. Model reliability is the primary issue of feature ablation analysis of features like URL structure, domain registration, and webpage behavior as well as comparative performance evaluation of classifiers like Naive Bayes, SVM, Random Forest, Decision Trees, and Gradient Boosting. Scalable, adaptive, and ethical phishing is proposed using NLP and blockchain-based hybrid solutions for long-term digital pathology systems.