URLCHECK: A License Web-Based Suspicious Uniform Resource Locator (URL) Detector for Business Organization

Abstract

The rise in malicious URL attacks has posed a major threat to business organizations, resulting in data breaches, financial losses, and privacy concerns. In addition, business owners and employees often lack the technical knowledge or tools to determine whether a URL is safe or malicious, increasing their vulnerability to phishing attacks. The proposed tool, URLCHECK is designed through Object-Oriented Analysis and Design (OOAD) as a web-based system integrating Python backend with frontend technologies to combat suspicious URL threats targeting businesses. It employs a machine learning model (TF-IDF Vectorizer and Logistic Regression) trained on external sources to classify URLs as "Safe" or "Suspicious", enhanced by URL breakdown analysis. The system features tiered access: Unlicensed users receive basic classifications, while Licensed business users obtain detailed reports with mitigation plans and archived URL analysis. Administrators can update the training model to improve accuracy. Accessible for daily organizational use, URLCHECK empowers employees and general users to identify suspicious URLs. By integrating advanced machine learning techniques and a clear user interface, the tool aims to improve organizational cybersecurity and safeguard sensitive data.