Machine Learning-Driven URL Analysis for Enhanced Threat Detection

Phishing, malware, defacement, and other malicious URLs are among the most significant online threats today. Traditional methods for detecting such threats rely on static blacklisting, which fails to adapt to the evolving malicious URL patterns. This article proposes a user-friendly approach using machine learning-based techniques, supported by an intuitive interface that can analyze URLs in real-time. Unlike existing solutions, which struggle with generalization or feature selection, our approach enhances adaptability by leveraging a wider range of structural, linguistic, and contextual URL features. Experiments have been performed using different machine learning models, including Decision Trees, K-nearest Neighbors, and Random Forest. From the results obtained, it has been observed that Random Forest does reasonably well with 89% in accuracy, proving that a lot of improvement by training the model on diverse data and labeled URLs. The tool first extracts numerical features from URL structures, so users can input any URL in real time and receive numerical ranking assessments regarding security risks. Future work will be on improving the adaptability of the model and enhancing user understanding through explainable AI techniques.