AI-BASED PHISHING WEBSITE DETECTION

Author(s)	Nishanshu Deshmukh, Nishant Sahu, Sahil Darokar, Sangam Jain, Prof. Ravi Kumar Mohane
Country	India
Abstract	Phishing remains a major cyber threat, with traditional defences like blacklists proving ineffective against rapidly evolving attacks. This review highlights the shift toward Artificial Intelligence (AI) and Machine Learning (ML) as adaptive solutions. It outlines a methodology involving feature engineering—using URL, domain, and content features—and applying classifiers such as Decision Trees, Random Forests, and Support Vector Machines (SVM). Comparative analysis shows ensemble models like Random Forest offer superior accuracy and generalisation. The paper surveys advancements from basic ML to modern Deep Learning and hybrid systems, identifies gaps such as vulnerability to adversarial attacks, and emphasizes the need for real-time, continuous learning detection mechanisms
Keywords
Discipline	Engineering
Published In	Volume 4, Issue 2, March-April 2026
Published On	2026-04-04

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Advanced International Journal of Multidisciplinary Research