URL Access Management System for Automated Request Filtering
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Solution Overview
Problem
Content monitoring applications face challenges in distinguishing between user-initiated and automatically generated HTTP requests, leading to unnecessary logging loads and obscured user activity patterns due to outdated and incomplete blacklists, as well as weak monitoring solutions when focusing on browser plug-in levels.
Innovation Solution
A URL access management system that monitors outbound network traffic, applies rules to differentiate between manually and automatically initiated requests based on factors like access rate, user idle status, and request source, categorizing requests and adjusting logging accordingly, with centralized data analysis for rule modification and blacklist/whitelist creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content monitoring applications log all HTTP requests to track user activity, then complete monitoring coverage is achieved, but logging load increases and user activity patterns become obscured by automatically generated requests
Solution Approach 1:
The patent extracts and separates automatically generated HTTP requests from user-initiated requests by analyzing request characteristics such as timing patterns, source identification, and behavior profiles. This extraction removes noise from the logging system, maintaining complete monitoring coverage while reducing logging load by filtering out robotic requests.
2Productivity
If blacklists are used to filter automatically generated requests, then logging load is reduced, but the blacklists become outdated and incomplete requiring continuous maintenance
Solution Approach 1:
The system performs self-service by automatically learning and adapting to new robotic request patterns through continuous analysis of HTTP request characteristics. Instead of relying on manually maintained blacklists, the system dynamically identifies and filters robotic requests based on observed behavior patterns, timing analysis, and source identification, eliminating the need for continuous blacklist updates.
3Productivity
If monitoring is performed at the browser plug-in level, then automatically generated requests are avoided, but users can easily evade monitoring by using unsupported browsers
Solution Approach 1:
The patent introduces an intermediary component that operates at the network level rather than requiring browser plug-ins. This intermediary monitors HTTP requests transparently, analyzing request characteristics to distinguish user-initiated from automatically generated requests. This approach maintains monitoring effectiveness while preventing user evasion, as it does not depend on browser support or user cooperation.
Data Source
AI summary
Outbound network traffic originating from a client computer is monitored, and requests to access URLs in the outbound network traffic are detected. Detected requests to access URLs are compared to each of a plurality of rules, in order to determine whether specific ones of the detected requests were initiated automatically by a process or manually by a human user. Where a rule of the plurality applies to a detected request, a rating concerning the request is adjusted. The rating indicates a likelihood as to whether the request was initiated automatically or manually. Depending upon whether the rating concerning the request reaches a threshold, the request is categorized as being either automatically initiated by a process, or manually initiated by a human user. Only those requests categorized as being manually initiated by a human user are logged.