Multi-Layer URL Filtering for Unknown Malicious Link Detection

The system performs preliminary actions by pre-training machine learning models with extensive URL features, patterns, and characteristics before actual filtering operations. The models are pre-equipped with knowledge of malicious URL patterns, domain structures, and phishing techniques, enabling them to quickly evaluate new URLs without requiring them to be on the blocklist. This preliminary preparation allows the system to detect unknown malicious URLs by comparing them against learned patterns rather than requiring exact matches.

The patent introduces machine learning models as intermediaries between the blocklist and the URL filtering process. These models act as a bridge that translates the static blocklist data into dynamic detection capabilities. The ML models analyze URL features, extract patterns, and make predictions about maliciousness, thereby mediating between the known blocklist entries and the unknown URLs that need to be evaluated. This intermediary layer enables the system to generalize from known malicious URLs to detect unknown variants.

The patent segments the URL filtering system into distinct functional layers, each handling specific aspects of analysis. The first layer performs blocklist matching for known malicious URLs. The second layer applies machine learning models for pattern recognition and prediction. The third layer conducts visual feature analysis of webpage content. This segmentation allows each layer to specialize in particular detection techniques, improving overall effectiveness while maintaining modularity that manages system complexity through clear separation of concerns.

The filtering system implements a nested structure where multiple filtering mechanisms are embedded within each other. The blocklist comparison is nested within the broader machine learning framework, which in turn is nested within the visual analysis system. Each layer operates independently but contributes to the overall detection decision. This nesting allows the system to build upon previous layers' capabilities without completely redesigning the architecture, managing complexity through hierarchical organization.