Detection of Fake Websites using AI

Abstract

The increasing reliance on online platforms has been accompanied by a surge in fraudulent websites aimed at phishing, financial scams, and identity theft. Traditional defenses, including blacklist databases and rule-based detection, are limited in their ability to recognize newly launched malicious domains. To address this, we propose an artificial intelligence–based framework that employs both machine learning and deep learning models for website authenticity verification. Key indicators such as URL composition, domain registration metadata, page content, and SSL certificate properties are analyzed to separate legitimate websites from deceptive ones. Experimental analysis demonstrates that advanced AI models, particularly ensemble approaches and deep neural architectures, achieve greater detection accuracy than conventional techniques. This framework can be integrated into browsers, financial systems, and email gateways to provide scalable, real-time protection and strengthen user security.