Hierarchical URL Pattern Matching for Zero-Day Detection
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Solution Overview
Problem
Existing technologies face challenges in accurately and efficiently detecting malicious URLs, particularly in identifying zero-day exploits, which can lead to high false negative rates and delayed detection.
Innovation Solution
A hierarchical structure constructor is used to combine URL patterns in a hierarchical structure, where nodes at each depth improve the certainty of classifying URLs as malicious or benign. This approach generates patterns that become increasingly descriptive at each depth, enhancing detection accuracy for malicious attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional URL detection methods are used, then the detection process is simple, but the accuracy of detecting malicious URLs (especially zero-day exploits) is low
Solution Approach 1:
The URL detection system is segmented into multiple hierarchical levels (root level, child levels) where each level analyzes specific URL components. The root level performs broad classification while child levels perform increasingly granular analysis of URL path components, query parameters, and other hierarchical elements. This segmentation enables accurate detection of zero-day exploits by breaking down the complex detection task into manageable hierarchical stages.
Solution Approach 2:
The invention introduces a hierarchical dimension to URL detection by organizing analysis into multiple levels depth-wise. Instead of treating URL analysis as a flat single-level process, the system creates a multi-dimensional hierarchical structure where each level adds another dimension of analysis depth. This allows the system to capture complex patterns across different URL components at varying depths, significantly improving detection accuracy for sophisticated attacks.
2Reliability
If broad URL pattern matching is used, then false negatives are high, but false positives are low
Solution Approach 1:
Different levels of the hierarchical structure apply different quality thresholds and analysis depths to different URL components. Child levels perform more stringent local analysis on specific URL segments (such as path components or query parameters) while the root level provides broader context. This local quality differentiation allows the system to maintain high reliability by applying appropriate scrutiny to different parts of the URL without uniformly increasing false positives across all matches.
Solution Approach 2:
The root level performs preliminary broad classification of URLs before they reach child levels. URLs that are clearly benign are filtered out early, preventing them from undergoing more granular analysis. This preliminary action reduces the workload on child levels and decreases false positives by ensuring that only URLs requiring deeper inspection are subjected to stringent child-level analysis, while maintaining high reliability through the progressive filtering approach.
3Measurement precision
If multiple URL features are analyzed in detail, then detection accuracy improves, but processing time increases
Solution Approach 1:
The detailed analysis of multiple URL features is segmented across hierarchical levels. The root level performs quick analysis of high-level URL characteristics, while child levels progressively analyze more granular features (path components, query parameters, etc.). This segmentation allows the system to maintain high pattern accuracy by analyzing multiple features, while reducing overall processing time by distributing the analysis workload across parallel hierarchical levels rather than sequentially analyzing all features to full depth for every URL.
Solution Approach 2:
The root level performs preliminary analysis of URL features before child levels conduct more detailed examinations. This preliminary action identifies URLs that clearly require deep analysis versus those that can be classified with minimal scrutiny. By performing preliminary feature analysis at the root level, the system maintains the capability for accurate multi-feature pattern matching while optimizing processing time by avoiding exhaustive analysis of all URLs at all levels.
Data Source
AI summary
A hierarchical structure constructor constructs a hierarchical structure that comprises nodes associated with feature sets patterns of URLs. Nodes at each depth are labelled as malicious, benign, or mixed for corresponding to URLs that are malicious, benign, or malicious and benign that match the corresponding patterns. Malicious feature set patterns are extracted from malicious nodes in the hierarchical structure. A URL analyzer operates inline by logging traffic sessions, extracting URLs from the logs, and matching the extracted URLs with the malicious feature sets patterns extracted from the hierarchical structure. The hierarchical structure is periodically updated with known malicious/benign URLs to improve quality of malicious URL detection.