Process Risk Classifier Dynamic Scoring
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Solution Overview
Problem
Existing computer systems lack an efficient method to accurately and dynamically assess and classify the risk posed by computer processes, which can lead to security risks such as unauthorized access and data corruption, as current methods rely heavily on static lists and do not account for real-time changes in process behavior.
Innovation Solution
A computer-implemented method that uses a process risk classifier to assess risks by analyzing process signatures, dependencies, and resource usage over time, generating dynamic risk scores based on differences and historical data, allowing for continuous monitoring and management of process risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static lists are used to assess process risk, then the assessment method is simple to implement, but the accuracy and responsiveness to real-time changes is poor
Solution Approach 1:
The patent transforms static risk assessment lists into dynamic, adaptive structures that automatically update based on real-time process behavior monitoring. The system continuously collects process data, detects anomalies through pattern recognition, and adjusts risk classifications dynamically, enabling the assessment mechanism to respond to changing process conditions while maintaining implementation feasibility through automated algorithms.
Solution Approach 2:
The patent implements feedback loops where process data is continuously monitored, analyzed, and used to update risk assessments. The system compares actual process behavior against expected patterns, detects deviations, and feeds this information back into the risk classification model, creating a closed-loop system that improves accuracy over time while remaining computationally efficient.
2Measurement precision
If dynamic risk assessment is implemented, then the accuracy and real-time responsiveness improves, but the system complexity increases
Solution Approach 1:
The patent divides the risk assessment system into modular components: data collection modules that gather process parameters, analysis modules that detect anomalies, classification modules that assign risk levels, and update modules that refine assessments. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling dynamic, accurate risk assessment through coordinated operation of specialized sub-systems.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically monitors its own performance, detects its own anomalies, and adjusts its own risk classifications without external intervention. The automated self-adjustment through embedded algorithms reduces the need for complex manual configuration and management, thereby lowering operational complexity while maintaining high assessment accuracy.
3Reliability
If process monitoring is continuously performed, then the detection of potential threats improves, but the computational resource consumption increases
Solution Approach 1:
The patent implements periodic monitoring at strategically determined intervals rather than continuous monitoring. The system adjusts the frequency of assessments based on process stability, risk levels, and resource availability, performing intensive analysis only when necessary and using lighter-weight checks during stable periods. This periodic approach maintains reliable threat detection while significantly reducing computational resource consumption compared to continuous monitoring.
Solution Approach 2:
The patent dynamically adjusts monitoring parameters such as sampling frequency, analysis depth, and detection thresholds based on current process conditions and risk levels. When processes are stable and low-risk, the system reduces monitoring intensity to conserve resources; when anomalies are detected or risk levels increase, the system intensifies monitoring to ensure reliable threat detection, thereby optimizing the balance between detection capability and resource usage.
Data Source
AI summary
In one implementation, a computer-implemented method includes receiving, at a process risk classifier running on a computer system, a request to determine a risk level for a particular process; accessing one or more signatures that provide one or more snapshots of characteristics of the particular process at one or more previous times; identifying one or more differences between the particular process in its current form and the one or more signatures; accessing information identifying previous usage of the computer system's resources by the particular process; determining a current risk score for the particular process based, at least in part, on (i) the one or more signatures for the particular process, (ii) the one or more differences between the particular process in its current form and the one or more signatures, and (iii) the previous usage of the resources; and providing the current risk score for the particular process.