Phishing is one of the most prevalent cyber threats, where attackers deceive users through fraudulent websites or URLs to steal sensitive information such as passwords and financial data. This paper presents a Machine Learning (ML)-based approach for phishing URL detection to enhance cybersecurity and protect users from online scams. The proposed system extracts various lexical, host-based, and content-based features from URLs, including domain length, presence of special characters, HTTPS usage, and URL age. These features are analyzed using supervised learning algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Logistic Regression to classify URLs as legitimate or phishing. Data preprocessing and feature selection are performed to improve model accuracy and reduce computational complexity. Experimental results show that ensemble and hybrid ML models outperform traditional blacklist-based methods in detection accuracy and adaptability. The system can be integrated into browsers or email filters to provide real-time protection against phishing attacks.
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