Detection of Malicious URL using Machine Learning and Flask Web Application

Abstract: The growing number of cyberattacks through malicious URLs has made automated threat detection a crucial component of cybersecurity. This paper presents a machine learning-based approach to detect and classify URLs as malicious or benign using URL-based features. We developed a web-based detection system using the Flask framework that enables users to input URLs and receive real-time threat predictions. The model is trained on a labelled dataset and utilizes features such as URL length, presence of symbols, digits, and suspicious substrings. Among several algorithms evaluated, the Random Forest classifier delivered the highest accuracy. The system architecture supports efficient integration of the model with the Flask application, ensuring minimal response time and a user-friendly interface. Experimental results demonstrate that our approach achieves a high level of accuracy, precision, and recall. This work offers a practical, lightweight solution for integrating machine learning-based URL detection into web services, browsers, or corporate gateways, thereby enhancing user safety against phishing and malware attacks.

Keywords: Malicious URL, Machine Learning, Flask, Web Application, Cybersecurity, URL Classification, Phishing Detection, Supervised Learning, Random Forest, Web Security Automation, Threat Intelligence, Feature Engineering.