Self-learning Machine Assessment Data Collection
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Solution Overview
Problem
Enterprise security systems face challenges in managing data collection for monitoring computing resources due to dynamic changes in data requirements, leading to issues of over-collection or under-collection, which strain resources and compromise security assessments.
Innovation Solution
A self-learning machine assessment system that automatically tunes data collection by deploying agents to collect machine characteristics, reporting data to a machine assessment system, which adjusts the collection rule set based on usage, optimizing the data scope without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection scope is expanded to ensure comprehensive security assessment, then measurement precision is improved, but resource utilization increases excessively
Solution Approach 1:
The patent implements dynamic data collection by adjusting the collection rule set based on changing assessment needs. The system transitions from static to dynamic configuration, where data collection parameters are automatically tuned according to the specific machine being assessed and the results of preliminary assessments, thereby optimizing resource utilization while maintaining assessment accuracy.
Solution Approach 2:
The system changes data collection parameters dynamically by modifying the collection rule set. Based on assessment results and machine-specific characteristics, the system adjusts which data points are collected, how frequently they are collected, and what depth of collection is required, thereby reducing unnecessary resource consumption while preserving critical security assessment capabilities.
2Use of energy by moving object
If data collection scope is reduced to minimize resource strain, then resource utilization is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies local quality by customizing data collection for each specific machine based on its characteristics and risk profile. Instead of uniform data collection across all machines, the system tailors the collection rule set to each machine's specific needs, collecting detailed data only where necessary for accurate security assessment while minimizing collection elsewhere, thus balancing resource utilization with assessment precision.
Solution Approach 2:
The system performs preliminary data collection and assessment to determine what additional data will be needed for comprehensive evaluation. By conducting initial assessments and analyzing results, the system proactively identifies which data points require deeper collection, allowing it to optimize the collection rule set before full-scale data gathering begins, thereby avoiding both over-collection and under-collection.
3Measurement precision
If data collection is manually configured to ensure comprehensive monitoring, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically configure and adjust its own data collection parameters. The machine assessment system autonomously tunes the collection rule set based on assessment needs and results, eliminating the need for manual configuration and management. This self-optimizing capability reduces operational complexity while maintaining high measurement precision through automated, data-driven decisions.
Solution Approach 2:
The system employs feedback mechanisms where assessment results inform subsequent data collection configurations. By continuously analyzing assessment outcomes and using that information to adjust the collection rule set, the system creates a closed-loop control mechanism that automatically optimizes data collection without manual intervention, thereby reducing management complexity while preserving assessment accuracy.
4Adaptability or versatility
If data collection is frequently adjusted to adapt to changing needs, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent enables the system to self-adjust data collection parameters automatically based on changing assessment requirements. The machine assessment system autonomously modifies the collection rule set in response to new information and evolving assessment needs, eliminating the need for complex manual reconfiguration processes. This self-service capability provides high adaptability while keeping system complexity manageable through automation.
Data Source
AI summary
Systems and methods are disclosed to implement a self-learning machine assessment system that automatically tunes what data is collected from remote machines. In embodiments, agents are deployed on remote machines to collect machine characteristics data according to collection rule sets, and to report the collected data to the machine assessment system. The machine assessment system assesses the remote machines using the collected data, and automatically determines, based on what data was or was not needed during the assessment, whether an agent's collection rule set should be changed. Any determined changes are sent back to the agent, causing the agent to update its scope of collection. The auto-tuning process may continue over multiple iterations until the agent's collection scope is stabilized. In embodiments, the assessment process may be used to analyze the remote machine to determine security vulnerabilities, and recommend possible actions to take to mitigate the vulnerabilities.