Volatility Score Testing Frequency Control
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Solution Overview
Problem
Existing security monitoring and testing techniques in large organizations face scalability issues due to rule-based approaches that require pre-defined patterns and scheduled testing, leading to inefficiencies and potential failure states due to incomplete data and repetitive testing.
Innovation Solution
Implementing a behavior change detection system using machine learning techniques to classify events based on volatility, which dynamically adjusts testing frequency based on measured changes in service behavior, independent of content changes, through the use of meta-domain descriptors and matrix execution for real-time event classification and volatility scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based monitoring is used, then security threats can be detected using pre-defined patterns, but the approach cannot scale and requires manual coding of rules based on expert knowledge
Solution Approach 1:
The patent replaces the mechanical rule-based monitoring system with a machine learning-based system. Instead of manually coding rules, the system uses ML models to automatically detect anomalies and behavior changes, enabling scalability while maintaining reliable security threat detection.
Solution Approach 2:
The system performs self-learning and self-adjustment through machine learning techniques. The ML models continuously learn from new data and automatically adapt to changing behaviors, eliminating the need for manual rule updates and enabling the system to scale without proportionally increasing manual effort.
2Reliability
If scheduled testing is used, then testing can be performed at regular intervals, but tests become repetitive and result in significant over-testing due to repetition of tests that pass cleanly
Solution Approach 1:
The patent implements dynamic testing frequency adjustment based on detected behavior changes. Instead of fixed scheduled testing, the system continuously monitors service behavior and adjusts testing intensity accordingly - increasing testing when behavior changes are detected and reducing testing when behavior is stable, thereby eliminating repetitive over-testing while maintaining reliable coverage.
Solution Approach 2:
The system uses feedback from behavior change detection to control testing frequency. The detected behavior changes serve as feedback that triggers or suppresses testing cycles, ensuring tests are performed only when necessary based on actual system state changes rather than on fixed schedules.
3Productivity
If event-based testing is used, then testing can be triggered by specific events, but code check-in and deployment may occur without developers being aware in complex distributed services
Solution Approach 1:
The patent introduces behavior change detection as an intermediary layer between service operations and testing. This intermediary continuously monitors service behavior and translates operational changes into actionable testing triggers, ensuring that testing responds to actual behavior changes while providing awareness to developers through structured notifications.
4Measurement precision
If full scanning of all events is performed, then patterns can be sought in data, but the approach demands significant computational resources and time
Solution Approach 1:
The patent extracts and focuses monitoring on behavior changes rather than scanning all events for patterns. By identifying and concentrating on changes in service behavior, the system achieves high detection accuracy without the computational burden of full event scanning, significantly reducing time and resource consumption.
Data Source
AI summary
Various implementations provide an approach to control testing frequency based on behavior change detection. Behavior change detection is utilized, instead of a pre-defined patterns approach, to look at a system's behavior and detect any variances from what would otherwise be normal operating behavior. In at least some implementations, a behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. In this way, the changes may be used to compute a volatility score, which the system uses to control testing frequency of one or more services, such as URLs that are part of a particular service.