AI-Powered Phishing Detection: Securing Web Navigation with Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802166Keywords:
AI-powered phishing detection, machine learning for web security, URL classification using AI, cybersecurity with machine learning, phishing attack prevention, intelligent threat detection, real-time phishing detectionAbstract
Phishing attacks are one of the most serious cybersecurity threats, where attackers create fraudulent websites to steal sensitive information such as login credentials, banking details, and personal data. Traditional protection methods like blacklists and antivirus tools are often ineffective because phishing websites are continuously evolving. This research proposes an AI-powered phishing detection system using Machine Learning techniques to improve web security. The proposed system analyzes URLs and website characteristics to distinguish between legitimate and phishing websites. A dataset containing more than 6000 URLs is used for training and evaluation. The system extracts multiple important features from URLs and webpage data, including lexical features, security- related attributes such as HTTPS usage and domain age, host- based information, and website popularity indicators. These features are preprocessed and converted into numerical form to train machine learning models effectively. Among the evaluated algorithms, the Gradient Boosting Classifier provided the best performance. The model achieved a detection accuracy of approximately 97. The proposed approach improves online security by detecting malicious websites before users interact with them. Unlike traditional blacklist systems, the machine learning model can identify new phishing patterns. This system can be further extended into browser extensions or scalable web services for enhanced phishing protection and safer web navigation.
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