Webpage Malicious Attribute Detection via URL Referenced Value
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Solution Overview
Problem
Traditional methods for detecting malicious attributes in webpages have low accuracy due to the increasing subtlety of malicious codes, making it difficult to detect new threats in a timely manner, which can result in harm before the malicious webpage is identified.
Innovation Solution
A method and system that acquire webpage query requests, crawl webpages, count a referenced value of URLs based on webpage contents, and call predetermined detection programs based on these values to improve detection accuracy, utilizing a combination of detection techniques such as signature-based, behavior-based, and sandbox filtering methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods (signature-based, behavior-based, sandbox filtering) are used, then detection coverage is maintained, but detection accuracy deteriorates due to increasingly subtle malicious codes
Solution Approach 1:
The patent introduces a referenced value as an intermediary indicator to assess webpage safety. Instead of directly detecting malicious codes, the system counts references to external URLs from multiple sources (search engines, other webpages, social media) to determine if a webpage is suspicious. This intermediary metric bridges the gap between traditional detection methods and the need for higher accuracy against subtle threats.
Solution Approach 2:
The system performs preliminary assessment by counting URL references before conducting full malicious code detection. By evaluating the referenced value first, the system can prioritize detection resources on high-risk webpages, improving overall detection accuracy while efficiently handling the increasing subtlety of malicious codes.
2Measurement precision
If multiple detection programs are deployed, then detection capability is improved, but system complexity increases
Solution Approach 1:
The patent applies different detection programs selectively based on the referenced value of each URL. High-risk URLs (with abnormal reference patterns) receive more intensive multi-program detection, while low-risk URLs use simpler detection methods. This local differentiation improves detection accuracy without uniformly increasing system complexity across all webpages.
Solution Approach 2:
The system changes the parameter of detection intensity based on the referenced value. By dynamically adjusting which detection programs are deployed according to the risk level indicated by reference counts, the system achieves high detection accuracy for critical cases while maintaining manageable overall system complexity.
Data Source
AI summary
A method for monitoring a malicious attribute of a webpage is disclosed. The method includes the following steps: acquiring webpage query requests submitted by a plurality of clients; crawling a webpage based on the webpage query requests and acquiring crawled webpage contents; counting up a referenced value of a URL based on the webpage contents; calling a predetermined detection program to detect a malicious attribute of the URL based on the a referenced value of the URL. The accuracy of detection can be improved by using the method for monitoring a malicious attribute of a webpage provided in the present disclosure. Furthermore, a system for monitoring a malicious attribute of a webpage is further disclosed.