Security Profile Generation Using External Data Clustering
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Solution Overview
Problem
Existing methods for assessing computing system security vulnerabilities are inadequate as they rely on unreliable external data or imprecise groupings, lacking direct access to internal data, which leads to incomplete and inaccurate security profiles.
Innovation Solution
A method using a clustering algorithm to organize existing computing systems into clusters based on both internal and external data, allowing for the classification of new systems and generation of accurate security profiles through statistical analysis of cluster metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If administrative access is obtained to collect comprehensive security information, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary approach by using publicly available external data (website content, domain registration information, security headers) as a mediator to infer internal security posture without requiring direct administrative access. This intermediary data serves as a proxy that bridges the gap between external observability and internal security assessment.
Solution Approach 2:
The patent replaces the mechanical system of direct administrative access with an automated computational system that collects, processes, and analyzes external data through web crawlers and clustering algorithms. This substitution eliminates the need for manual access while maintaining assessment capability through automated information gathering and machine learning-based inference.
2Ease of operation
If questionnaire is provided to administrator for security information, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The system performs self-service by automatically collecting security-relevant information from public sources without requiring administrator input. Web crawlers autonomously gather data about the target system's external footprint, security headers, and domain information, eliminating reliance on potentially misleading administrator-provided information while maintaining ease of operation.
Solution Approach 2:
The patent implements feedback mechanisms by using clustering algorithms that compare the target system's external characteristics against known patterns from trained data. The system receives feedback from the clustering process that validates or adjusts security posture assessments based on how closely the target matches established security profiles of similar systems.
3Ease of operation
If broad grouping by industry is used for security estimation, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the broad industry grouping into finer clusters based on specific external characteristics such as website content, security headers, domain registration details, and technical stack. This segmentation creates more homogeneous groups that better reflect actual security postures, improving measurement precision while maintaining the operational simplicity of automated classification.
Solution Approach 2:
The patent changes the parameters used for grouping from broad industry categories to specific technical parameters including security header configurations, website structure characteristics, domain registration information, and external footprint metrics. These parameter changes enable more precise clustering that better predicts internal security posture while maintaining automated ease of operation.
Data Source
AI summary
Methods and systems are provided for generating a security profile for a new computing system. One example method generally includes obtaining, over a network, information associated with a plurality of existing computing systems and generating, by a clustering algorithm, a set of clusters based on the information associated with the plurality of existing computing systems. The method further includes obtaining external data associated with the computing system and classifying the computing system into a cluster in the set of clusters based on the external data associated with the computing system. The method further includes determining the security profile based on statistics associated with the cluster and transmitting, over the network, an indication of the security profile.