Authors : Akileshwari A.; Akash A.; Eswar M. S.; Abishek G. P.

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/4racy2br

Scribd : https://tinyurl.com/3mdadph9

DOI : https://doi.org/10.38124/ijisrt/26mar1467

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Phishing attacks are among the most prevalent cybersecurity threats, exploiting users through deceptive URLs and malicious websites to steal sensitive information such as login credentials, financial data, and personal details. This paper proposes an AI-powered URL security and phishing detection system that analyzes URLs using machine learning techniques and feature-based evaluation. The system extracts critical attributes such as HTTPS usage, SSL certificate validity, domain characteristics, URL structure, and WHOIS information. A trained machine learning model processes these features to classify URLs as legitimate or phishing. The application is implemented as a web-based system using Flask, providing real-time analysis and user-friendly interaction. Experimental results demonstrate improved accuracy and efficiency in detecting phishing URLs, making the system a reliable tool for enhancing online security and safe browsing practices.

Keywords : Phishing Detection; URL Security; Machine Learning; Cybersecurity; Flask; SSL Certificate; WHOIS.

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