Trusted Accessor Whitelisting for Sensitive URI Protection
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Solution Overview
Problem
Computer systems connected to public networks are vulnerable to attacks that aim to gain unauthorized access, disrupt services, or steal data, with sensitive Uniform Resource Identifiers (URIs) posing a higher risk if accessed by malicious actors.
Innovation Solution
The system identifies and restricts access to sensitive URIs by using a combination of manual and AI techniques to determine trusted accessors, filtering out untrusted ones, and maintaining a whitelist for authorized access, thereby enhancing protection by analyzing historical data from multiple websites to identify patterns of malicious behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access to sensitive URIs is restricted to a whitelist of trusted accessors, then security against unauthorized access is improved, but system complexity increases due to whitelist maintenance and accessor classification
Solution Approach 1:
The system performs preliminary classification of accessors as trusted or untrusted by analyzing historical access patterns before the actual access request is processed. This advance classification enables the whitelist to be pre-populated with trusted accessor identifiers, allowing rapid security decisions without complex real-time analysis during access requests.
Solution Approach 2:
The system automatically classifies accessors and maintains the whitelist without requiring manual intervention. By analyzing historical data from multiple websites and automatically identifying trusted accessors based on their access patterns, the system eliminates the need for manual whitelist management, reducing operational complexity while maintaining security.
2Measurement precision
If historical data from multiple websites is analyzed to identify trusted accessors, then accuracy of accessor classification is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs accessor classification during a preliminary learning phase using historical data from multiple websites before production use. By pre-processing and analyzing historical access patterns across multiple sites, the system builds a trained model that can rapidly classify new accessors without requiring real-time analysis of all historical data, thus reducing processing time during actual access requests.
Solution Approach 2:
The system analyzes historical data from multiple different websites simultaneously to identify trusted accessors. By pooling data across multiple sites and using a universal classification approach that works across different websites, the system achieves higher classification accuracy through larger data samples while the multi-functional analysis framework efficiently processes diverse data sources.
3Reliability
If untrusted accessors are filtered out using thresholds and machine learning techniques, then security against malicious actors is improved, but computational complexity increases
Solution Approach 1:
The system performs machine learning-based accessor classification during a preliminary learning phase using historical data. By pre-training the machine learning model with historical access patterns from multiple websites, the system creates a ready-to-use classification framework that can rapidly evaluate new accessors without requiring complex real-time machine learning computations, thus reducing operational computational complexity while maintaining high security.
Solution Approach 2:
The system creates a simplified representation or model of trusted accessor characteristics based on historical data analysis. By copying and storing the learned patterns and characteristics of trusted accessors in a structured format (such as the whitelist with associated metadata), the system enables rapid security decisions without repeatedly executing complex machine learning algorithms, reducing computational complexity during access requests.
Data Source
AI summary
“Sensitive” URIs for a website can be determined. Access attempts to a sensitive URI can be extracted from server logs. As used herein, sensitive URIs are URIs which if breached are likely to result in harm to the website owner. Access to sensitive URIs can be restricted to trusted accessors. Trusted accessors can be determined by filtering out untrusted accessors using thresholds and/or machine learning techniques. After filtering out untrusted accessors, any remaining accessors can be identified as trusted accessors. Trusted accessors can be added to a whitelist. Access requests to access-restricted URIs by an accessor not in the whitelist can be denied and an alert can be generated. Access requests to access-restricted URIs by an accessor in the whitelist can be granted.