Bayesian URL Classification for Malicious Site Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional full-scan methods for detecting malicious web pages are slow, expensive, and inefficient, as they require analyzing vast numbers of web pages, many of which remain unanalyzed due to the breadth-first traversal approach, and content-based features indicative of maliciousness can change frequently, making manual updates challenging.
Innovation Solution
A URL-focused crawling system uses Bayesian classification to assign maliciousness probabilities to URLs, modifying traversal priorities to direct higher priority analysis towards pages with higher maliciousness probabilities, allowing for a more focused and efficient traversal order without examining page content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional full-scan methods are used to detect malicious web pages, then comprehensive coverage of all pages is achieved, but the detection speed and efficiency are extremely slow and expensive
Solution Approach 1:
The patent segments the web page analysis process into two distinct phases: URL-level Bayesian classification (coarse filtering) and page-level maliciousness detection (fine analysis). This segmentation allows the system to quickly filter out benign URLs before performing expensive content analysis, thereby maintaining detection coverage while dramatically improving detection speed and reducing computational costs.
Solution Approach 2:
The patent applies partial action by performing Bayesian classification only on URLs rather than analyzing complete page content for all pages. This partial analysis at the URL level provides sufficient filtering capability to identify potentially malicious pages without the need for exhaustive content analysis of every page, thus achieving efficient detection with reduced resource consumption.
2Reliability
If breadth-first traversal order is used to scan web pages, then systematic coverage is achieved, but too many pages remain unanalyzed at any given time
Solution Approach 1:
The patent introduces dynamic prioritization by assigning time-decay weights to URLs in the traversal queue. URLs that have been in the queue longer receive lower priority scores, causing the system to dynamically adjust traversal order based on both maliciousness probability and queue position. This dynamic mechanism ensures that high-value targets are analyzed promptly while maintaining systematic coverage of the entire web space over time.
3Measurement precision
If content-based features are used to identify malicious sites, then detection accuracy is improved, but the cost of preliminary content analysis is expensive
Solution Approach 1:
The patent segments the analysis process into URL-level Bayesian classification using structural features and page-level maliciousness detection using content-based features. This segmentation allows the system to use inexpensive URL analysis as a filter before applying expensive content analysis only to suspicious cases, thereby maintaining detection accuracy while significantly reducing overall analysis costs.
Solution Approach 2:
The patent introduces Bayesian classification of URLs as an intermediary step between initial page discovery and detailed maliciousness analysis. This intermediary layer uses lightweight URL features to pre-screen pages, acting as a gatekeeper that filters out benign pages before they reach the expensive content analysis stage, thus reducing overall computational cost while preserving detection accuracy.
4Reliability
If manual updates are used to track content-based features indicative of maliciousness, then detection effectiveness is maintained, but the frequency of updates cannot match the rapid changes in malicious strategies
Solution Approach 1:
The patent implements self-service through automated Bayesian classification that continuously learns from new data without requiring manual intervention. The system automatically updates its understanding of malicious patterns by processing new URL-page pairs and adjusting classification parameters, enabling it to adapt to changing malicious strategies in real-time rather than relying on periodic manual updates.
Solution Approach 2:
The patent incorporates feedback mechanisms where detection results from analyzed pages are fed back into the Bayesian classification system. This feedback loop allows the system to continuously refine its URL classification model based on actual maliciousness outcomes, enabling automatic adaptation to new attack patterns and strategies as they emerge in the wild.
Data Source
AI summary
For each page of a set, a Bayesian classification of the URL associated with the page is performed, and a maliciousness probability is assigned to the URL based on the Bayesian classification. A traversal priority is assigned to each page of the set, the assigned traversal priorities initially directing a breadth first traversal of the set of pages. The assigned traversal priorities of a subset of the pages of the set are modified to direct higher priority traversals, responsive to the maliciousness probabilities of the URLs corresponding to the pages of the subset. Each page of the set is traversed in the order specified by the traversal priorities, and analyzed during traversal to determine whether the page is malicious.