URL Reputation Queue Prioritization for Faster Phishing Detection
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Solution Overview
Problem
Phishing attacks are challenging to detect in real-time due to the high volume of URLs and the ability of phishing sites to evade reputation caches by frequently changing their URLs, leading to delayed classification and increased user exposure to potential threats.
Innovation Solution
Implement a rough analysis engine that performs a visual similarity check between suspicious URLs and known phishing targets, prioritizing these URLs for expedited analysis to reduce the time to assign a reliable reputation, thereby minimizing user exposure to phishing sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive URL analysis is performed to improve phishing detection accuracy, then detection reliability is improved, but analysis time and processing speed worsen
Solution Approach 1:
The URL analysis process is divided into multiple independent analysis components (domain analysis, path analysis, parameter analysis, etc.), each executed selectively based on rough analysis results. This segmentation allows the system to perform comprehensive analysis only when necessary, improving both accuracy and efficiency.
Solution Approach 2:
A rough analysis is performed first to quickly identify potentially malicious URLs before conducting comprehensive analysis. This preliminary filtering action reduces the number of URLs requiring full analysis, thereby maintaining high detection accuracy while reducing overall processing time.
2Measurement precision
If more analysis resources are allocated to improve detection capability, then detection precision is improved, but system complexity worsens
Solution Approach 1:
The analysis system dynamically adjusts the depth and scope of URL analysis based on risk assessment. URLs flagged as high-risk undergo comprehensive multi-dimensional analysis, while low-risk URLs receive minimal analysis. This dynamic approach improves detection precision for malicious URLs while avoiding unnecessary complexity for benign URLs.
Solution Approach 2:
Different analysis methods and resource allocations are applied to different parts of the URL structure and different risk categories. High-risk domains receive intensive analysis resources, while low-risk domains receive standard processing, optimizing precision without uniformly increasing system complexity.
3Reliability
If all URLs are analyzed comprehensively to ensure no phishing sites are missed, then detection coverage is improved, but processing speed worsens
Solution Approach 1:
The system performs preliminary rough analysis on all URLs to categorize them by risk level before conducting detailed analysis. This preliminary action enables the system to maintain comprehensive coverage for high-risk URLs while rapidly processing low-risk URLs, thus improving both detection coverage and processing speed.
Solution Approach 2:
The system applies partial comprehensive analysis only to URLs that require it based on rough analysis results. Instead of performing full comprehensive analysis on all URLs, it selectively applies detailed analysis to suspicious cases, maintaining detection coverage while significantly improving overall processing speed.
Data Source
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AI summary
There is disclosed in one example a computing apparatus, including: a processor and a memory; a uniform resource locator (URL) reputation store; a network interface; and instructions encoded within the memory to instruct the processor to: receive via the network interface a request for a reputation for a URL; query the URL reputation store and determine that the URL does not have a known reliable reputation; add the URL to a URL analysis queue; perform a rough analysis of the URL, and determine from the rough analysis that the URL potentially is for a phishing website; and move the URL ahead in the analysis queue.