Malware URL Detection for Enhancing Web Quality

The proliferation of internet-based services has fundamentally transformed daily communications and transactions, yet this digital transformation has simultaneously created unprecedented opportunities for cybercrime through malicious URLs. Particularly concerning is the rising prevalence of fraudulent links disseminated through SMS and messaging platforms, which pose significant threats to user privacy, financial security, and data integrity. Despite the critical nature of this challenge, current detection mechanisms often lack real-time capability and user accessibility. This research proposes a novel hybrid framework that leverages advanced machine learning and deep learning architectures to detect and classify malicious URLs in real-time messaging environments. The proposed system integrates a Random Forest classifier with a Convolutional Neural Network (CNN) to analyze URL characteristics across multiple dimensions, including lexical features, host-based attributes, and content-based patterns. Our approach uniquely combines traditional machine learning's interpretability with deep learning's pattern recognition capabilities, achieving enhanced detection accuracy while maintaining computational efficiency. The framework is implemented through a user-centric mobile application that seamlessly integrates with existing messaging platforms, providing immediate threat assessment of incoming URLs. Preliminary results indicate promising detection rates across various attack vectors, including phishing, malware distribution, and domain spoofing. This research contributes to the cybersecurity domain by offering an adaptive, scalable solution for protecting users against evolving URL-based threats in their daily digital interactions.