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September 10, 2025

PhishGuard: A Smart Approach for Malicious URL Identification with ML

Key Points

The proposed system achieved over 90% accuracy in identifying phishing URLs and enhancing user security.
Using algorithms like Random Forests and Neural Networks, the model classifies URLs effectively, adjusting as new data is reported.
The machine learning model is deployed on a user-friendly, non-profit website for real-time phishing reporting and identification.
Machine learning significantly strengthens defenses against sophisticated phishing attacks, crucial for today’s internet security.

Abstract

With the rise of mobile device usage and increasing reliance on the internet, most real-world activities—banking, shopping, and communication—have transitioned online. While this digital shift enhances convenience, it also raises cybersecurity concerns, particularly phishing attacks. Phishing involves deceptive websites mimickinglegitimateonestostealsensitive user data like passwords and credit card numbers. Traditional security tools often fail to detect such sophisticated, zero-day attacks. This study suggests a machine learnings based phishing detections system that uses algorithms such as Random Forests, SVM, Decision Tree, Naïve Bayes and Neural Networks. These models analyze URL features to classifysitesaslegitimateorphishing.The system is deployed on a free, ad-free, non- profit website that also allows users to report suspicious URLs, enhancing accuracy over time. Tested on diverse datasets, the models achieved over 90% accuracy.Thisresearchhighlightsmachine learning’sroleineffectivelycombating phishing threats and strengthening cybersecuritydefenses.

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