Author(s) :

Homam ElTaj.

Volume/Issue :

Abstract :

Malicious URLs continue to pose a significant cybersecurity threat, frequently bypassing conventional detection systems through URL masking and rapid domain switching. This paper proposes a conceptual framework for a lightweight, real-time browser extension designed to block harmful URLs using a dynamically updated blacklist. Beyond detection, the system integrates a user-awareness module offering contextual security guidance and regulatory resources, promoting safer online behavior. The proposed extension aims to address key limitations in existing tools by offering client-side protection with minimal performance overhead and a community driven reporting mechanism. Although the current design employs deterministic logic, future development will incorporate machine learning to enhance adaptability and improve classification of emerging threats. By combining real-time detection, user education, and planned AI integration, the proposed solution contributes a practical and forward-looking approach to strengthening browser level web security.

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