Automated URL Security Analysis Using Malice Patterns
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Solution Overview
Problem
Current methods for identifying malicious URLs are either subjective and labor-intensive or rely on costly detonation virtual machines, making them inefficient for large-scale analysis without human review.
Innovation Solution
Implementing automated URL security analysis tools that analyze URL substrings using predefined and inferred malice patterns, including machine learning models, to assign maliciousness risk indicators and enhance cybersecurity by allowing or blocking URL usage based on these indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated URL security analysis using malice patterns is implemented, then productivity and consistency of URL analysis improve, but measurement precision and reliability may be reduced compared to human review
Solution Approach 1:
The URL analysis process is segmented into multiple independent malice patterns (e.g., pattern 1 through pattern 10 in the patent), each checking for specific malicious characteristics. This segmentation allows automated analysis to evaluate URLs against multiple criteria simultaneously, improving both productivity and maintaining precision through comprehensive pattern matching.
Solution Approach 2:
The system changes parameters by analyzing different aspects of URLs (domain name, path, query parameters, etc.) using multiple malice patterns. Each pattern checks specific parameters of the URL, and the combined results provide a comprehensive maliciousness assessment that maintains accuracy while enabling automated high-volume analysis.
2Reliability
If detonation virtual machines are used for URL analysis, then reliability of malicious URL detection improves, but device complexity and cost increase
Solution Approach 1:
The patent extracts and analyzes specific malicious patterns directly from URL text without requiring full URL detonation in virtual machines. By taking out and analyzing only the relevant substrings and patterns (e.g., domain names, paths, parameters) against predefined malice patterns, the system maintains reliable detection while eliminating the need for complex virtual machine infrastructure.
Solution Approach 2:
Instead of using actual detonation virtual machines, the system creates a simplified copy-based analysis model that evaluates URLs against stored malice patterns. This copying approach maintains the essential detection functionality while removing the complex virtual machine layer, reducing device complexity and cost.
3Measurement precision
If subjective human review is used for URL analysis, then measurement precision improves, but productivity and ease of operation deteriorate
Solution Approach 1:
The system implements self-service automated URL analysis using malice patterns that independently evaluate URLs without requiring human review. The multiple patterns work together to provide comprehensive analysis, maintaining precision while dramatically improving productivity by eliminating the bottleneck of manual human assessment.
4Measurement precision
If multiple malice patterns are applied to each URL, then measurement precision and reliability improve, but device complexity increases
Solution Approach 1:
The patent merges multiple malice patterns into a unified automated analysis system that processes URLs through all patterns simultaneously. The patterns are combined to evaluate different aspects of URLs (domain, path, parameters, etc.), and their results are integrated to provide a comprehensive maliciousness assessment, improving precision while managing complexity through systematic integration.
Data Source
AI summary
Cybersecurity enhancements help avoid malicious Uniform Resource Locators (URLs). Embodiments may reduce or eliminate reliance on subjective analysis or detonation virtual machines. URL substrings are automatically analyzed for maliciousness using malice patterns. Patterns may test counts, lengths, rarity, encodings, and other inherent aspects of URLs. URLs may be analyzed individually, or in groups to detect shared portions, or both. URL analysis may use or avoid machine learning, and may use or avoid lookups. Malice patterns may be used individually or in combinations to detect malicious URLs. Analysis results may enhance security through blocking use of suspect URLs, flagging them for further analysis, or allowing their validated use, for instance. Analysis results may also be fed back to further train a machine learning model or a statistical model.