Web Threat Protection System for Malicious URL Detection
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Solution Overview
Problem
Detecting malicious websites in the vast and dynamic public computer network like the Internet is challenging due to their widespread presence and frequent changes, making it difficult to maintain an effective listing of safe and harmful locations.
Innovation Solution
A web threat protection system that receives candidate URLs from various sources, selects them for further investigation using crawlers, analyzes retrieved objects to determine malicious or safe status, and builds a security database to provide safe navigation options, leveraging proactive and reactive URL identification methods and integrating with search engines for data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If crawlers are sent to investigate candidate URLs to detect malicious websites, then detection accuracy is improved, but system resource consumption and processing time increase
Solution Approach 1:
The system performs preliminary actions by maintaining a database of known malicious website characteristics, patterns, and signatures before actual detection occurs. When a candidate URL is received, the system first checks against this pre-built knowledge base to quickly identify obvious threats without requiring full crawler investigation, thus reducing processing time while maintaining detection accuracy for known threat patterns
Solution Approach 2:
The detection process is segmented into multiple stages: initial filtering using pre-built patterns, selective crawler deployment for suspicious URLs, and detailed analysis only for high-risk candidates. This segmentation allows the system to apply full detection resources only where necessary, reducing overall processing time while maintaining high detection accuracy for malicious sites
2Reliability
If the system monitors and investigates all candidate URLs comprehensively, then detection coverage is improved, but system complexity increases
Solution Approach 1:
The system applies different levels of investigation quality to different candidate URLs based on their risk characteristics. High-risk URLs receive comprehensive crawler analysis, medium-risk URLs receive pattern-matching analysis, and low-risk URLs are quickly filtered or dismissed. This local differentiation of analysis depth reduces system complexity while maintaining high detection coverage through targeted resource allocation
Solution Approach 2:
The system introduces an intermediary classification layer that categorizes candidate URLs into risk tiers before applying detailed analysis. This intermediary step simplifies the overall system architecture by creating a structured pipeline that routes different URL types to appropriate analysis methods, reducing complexity while ensuring comprehensive coverage through multi-level processing
3Reliability
If the system maintains an updated listing of malicious locations, then user protection is improved, but data maintenance burden increases
Solution Approach 1:
The system implements self-service mechanisms where crawlers automatically discover new malicious websites and update the malicious locations database without manual intervention. The system continuously self-updates its threat intelligence by analyzing crawled content, generating new malicious site entries, and refining detection patterns automatically, thus reducing data maintenance burden while improving user protection through real-time updates
Solution Approach 2:
The system incorporates feedback loops where detection results from crawled URLs are fed back into the database to continuously refine the malicious locations listing. Successful detections automatically update the database, which in turn improves future detection accuracy. This feedback mechanism reduces manual maintenance burden by making the system self-improving while enhancing user protection through continuously refined threat data
Data Source
AI summary
A web threat protection system may receive candidate uniform resource locators (URLs) from several URL sources. The candidate URLs may be received in a submission database. At least a portion of the candidate URLs is selected for further investigation by sending crawlers to retrieve objects from the selected URLs. The retrieved objects may be analyzed to determine whether they are malicious or good (i.e., not malicious). The result of the analysis may be used to build a security states database that includes security information of the selected URLs. Good URLs may be included in a safe URL sphere, which may be used to navigate to good websites on the Internet.