Implementation of a Browser Extension with Multi-Layer Security Filtering Architecture for Real-Time Phishing Website Detection
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The increasingly sophisticated evolution of phishing attacks poses a significant challenge for cybersecurity systems, particularly regarding zero-day threats that frequently evade traditional blacklist detection. This study proposes the development of a browser extension with a Multi-Layer Security Filtering architecture to provide real-time protection. The system is designed across three defensive layers: Layer 1 employs the PhishTank API as a blacklist filter, Layer 2 uses the Tranco List API for whitelist verification, and Layer 3 applies an Extreme Gradient Boosting (XGBoost) model to detect emerging threats based on 22 lexical URL features. The system architecture is built on a Thin-Client model with a FastAPI backend to execute all security logic centrally on the server-side. The XGBoost model achieves an accuracy rate of 97.73% at an optimal threshold of 0.7 and an AUC score of 0.9927. Functionality testing demonstrates the system's success in performing automatic blocking and visual intervention through a blocking page. Furthermore, memory consumption testing validates the efficiency of the Thin-Client architecture, as the extension is proven to avoid persistent memory footprint allocation during URL capture, thereby ensuring lightweight browser performance. This implementation demonstrates that integrating list-based methods with artificial intelligence provides a proactive, accurate, and efficient detection response.
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