Network Asset Information Management via Quality Scores
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Solution Overview
Problem
Current asset and vulnerability management in networks faces challenges in generating accurate, detailed, and up-to-date information efficiently, requiring automated tools to integrate multiple data sources while minimizing computing effort and ensuring prompt action on vulnerable assets.
Innovation Solution
A method for automatic asset information management in networks involves identifying assets, assigning criticality, resiliency, granularity, confidence, and freshness values, calculating quality scores, and optimizing these scores through iterative deep packet inspection requests to ensure accurate and timely data freshness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated tools integrate multiple data sources to maximize coverage and detail of asset information, then the accuracy and freshness of asset information is improved, but the computing effort and processing complexity increases significantly
Solution Approach 1:
The patent segments asset information into discrete properties (e.g., firmware version, operating system, hardware configuration) and processes them individually through quality scores. This allows the system to handle large volumes of data from multiple sources without overwhelming computational complexity, as each property can be evaluated and updated independently rather than processing all asset data as a monolithic entity.
Solution Approach 2:
The patent introduces quality scores as a parameter to prioritize and filter asset information processing. By calculating quality scores based on multiple factors (data freshness, source reliability, completeness), the system can dynamically adjust which asset properties require immediate processing versus those that can be updated later, thereby reducing overall computing effort while maintaining information accuracy.
2Measurement precision
If automated tools perform complete vulnerability scans and assess all assets in detail, then the detection precision of vulnerabilities is improved, but the time required and productivity loss increases
Solution Approach 1:
The patent applies quality scores to prioritize asset processing based on risk factors, asset criticality, and data freshness. This allows the system to focus vulnerability scanning efforts on high-value targets first, rather than uniformly scanning all assets. Assets with lower quality scores or lower business criticality can be scanned with reduced intensity or deferred to lower-priority time slots, reducing overall assessment time while maintaining detection precision for critical assets.
Solution Approach 2:
The patent implements partial vulnerability assessment by using quality scores to determine the depth and scope of scanning for each asset. For assets with high quality scores and critical business value, complete detailed scans are performed. For assets with lower quality scores or non-critical status, abbreviated or targeted scans are sufficient, reducing total scanning time while maintaining adequate detection precision for the overall asset portfolio.
3Loss of time
If the system updates all asset properties continuously to maintain freshness, then the information freshness is improved, but the computational burden and energy consumption increases
Solution Approach 1:
The patent uses quality scores that include a freshness component to dynamically determine update priorities. Instead of continuously updating all asset properties at fixed intervals, the system adjusts update frequency based on the quality score of each property. Properties with deteriorating quality scores (due to aging data) trigger targeted updates, while properties with high quality scores maintain their current values, significantly reducing computational burden and energy consumption compared to uniform continuous updating.
Solution Approach 2:
The patent implements periodic quality score calculations and selective updates based on thresholds. Rather than continuous real-time updates of all asset properties, the system periodically evaluates quality scores and only triggers updates when properties fall below certain freshness or quality thresholds. This periodic, event-driven approach maintains information freshness while minimizing unnecessary computational operations and energy consumption.
Data Source
AI summary
Disclosed are methods for automatic retrieving and managing assets information in a network. The method includes identifying, defining, and valuing stored assets in a network. An asset is defined and identified by assigned values that include criticality values, resiliency values, granularity values, and freshness values that may be selected from a predefined set of values. The assets are valued by an overall quality score that is determined through computerized data processing and optimized by updating asset properties.