Hybrid URL Classification With Visual Comparison for Phishing Detection
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Solution Overview
Problem
Existing systems face challenges in automatically identifying malicious links in electronic communications due to limited computing resources, particularly in balancing network security with efficient resource utilization.
Innovation Solution
A computing platform that processes URLs using a combination of human-engineered features and deep learned features, generating a concatenated vector representation to classify URLs as malicious or legitimate using a URL classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive URL analysis techniques are integrated into automated security processes, then detection accuracy improves, but computing resource consumption increases
Solution Approach 1:
The URL analysis system is divided into multiple independent modules: URL parsing module, feature extraction module, machine learning classification module, and visual comparison module. Each module processes specific aspects of URL analysis independently, allowing the system to achieve comprehensive detection accuracy while managing computing resources through modular operation rather than monolithic processing.
Solution Approach 2:
The system applies different levels of analysis depth to different URLs based on risk assessment. For low-risk URLs, basic feature extraction and classification are performed. For high-risk URLs, more computationally intensive visual comparison and deep learning analysis are applied. This partial action approach ensures high detection accuracy for critical cases while conserving computing resources on routine traffic.
2Reliability
If multiple classification thresholds and actions are implemented, then security response effectiveness improves, but system complexity increases
Solution Approach 1:
The system implements dynamic threshold adjustment and adaptive response selection. Classification thresholds are not fixed but can be adjusted based on historical data, threat patterns, and current security context. The multi-threshold system (first threshold for high-confidence malicious URLs, second threshold for borderline cases) allows the system to adapt its response intensity to the specific risk level of each URL, improving security effectiveness while managing complexity through structured decision logic.
Solution Approach 2:
The system pre-defines multiple classification thresholds and corresponding security actions in advance. Rather than determining responses in real-time during URL analysis, the threshold levels and associated actions (block, quarantine, allow with monitoring, etc.) are established beforehand. This preliminary configuration reduces runtime decision complexity while maintaining high security response effectiveness through pre-planned, context-appropriate actions.
Data Source
AI summary
Aspects of the disclosure relate to detecting and identifying malicious sites using machine learning. A computing platform may receive a uniform resource locator (URL). The computing platform may parse and/or tokenize the URL to reduce the URL into a plurality of components. The computing platform may identify human-engineered features of the URL. The computing platform may compute a vector representation of the URL to identify deep learned features of the URL. The computing platform may concatenate the human-engineered features of the URL to the deep learned features of the URL, resulting in a concatenated vector representation. By inputting the concatenated vector representation of the URL to a URL classifier, the computing platform may compute a phish classification score. In response to determining that the phish classification score exceeds a first phish classification threshold, the computing platform may cause a cybersecurity server to perform a first action.