Hybrid Anomaly Detection Framework Reducing False Positives
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Solution Overview
Problem
Conventional anomaly detection systems generate a high number of false alerts due to their inability to account for contextual nuances, leading to resource wastage in root cause analysis, and are often not scalable or adaptable to complex systems.
Innovation Solution
A hybrid framework that combines statistical anomaly detection with machine learning and human Subject Matter Expert input to differentiate between true and false anomalies by determining intrinsic and extrinsic features, iteratively learning to filter out false alerts and identify true anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical anomaly detection systems are used, then anomaly detection capability is provided, but the number of false alerts increases significantly
Solution Approach 1:
The system segments the anomaly detection process into multiple independent components: statistical anomaly detection, feature extraction (intrinsic and extrinsic), machine learning classification, and SME validation. Each component handles a specific aspect, allowing the system to maintain detection sensitivity while filtering false alerts through progressive refinement.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between statistical anomaly detection and final anomaly classification. This intermediary learns from historical data and SME feedback to distinguish true anomalies from false alerts, reducing false positive rates while maintaining detection accuracy.
2Reliability
If conventional statistical anomaly detection is used, then detection rigor is maintained, but scalability to complex systems with contextual nuances is limited
Solution Approach 1:
The system transitions from static statistical thresholds to dynamic, adaptive anomaly detection. The machine learning model continuously learns from new data and SME feedback, adapting to changing system conditions and contextual nuances. This enables scalability to complex systems while maintaining detection rigor through iterative refinement.
Solution Approach 2:
The patent creates a composite anomaly detection system that combines multiple approaches: statistical methods provide rigorous baseline detection, machine learning adds adaptability to contextual patterns, and SME input contributes domain expertise. This composite structure enables the system to handle complex systems while maintaining detection rigor.
3Reliability
If anomaly detection systems report all statistical anomalies, then comprehensive monitoring is achieved, but resource waste in root cause analysis increases
Solution Approach 1:
The system applies partial action by selectively processing only those anomalies that the machine learning model classifies as potential true anomalies. Instead of analyzing all statistical anomalies, the system performs comprehensive monitoring through automated classification and directs root cause analysis resources only to high-probability true anomalies, significantly reducing resource waste.
Solution Approach 2:
The system implements feedback loops where SME validation of classified anomalies provides learning signals to the machine learning model. This feedback mechanism continuously improves classification accuracy, enabling the system to maintain comprehensive monitoring while increasingly accurately filtering out false alerts to reduce resource consumption in root cause analysis.
Data Source
AI summary
The present disclosure describes systems and methods that provide a hybrid framework for augmenting statistical anomaly detection with contextual features, machine learning and human Subject Matter Expert (SME) input to learn significant characteristics of true anomalies for which alerts should be generated. The framework presented herein is domain agnostic and independent of the underlying statistical anomaly detection technique or the machine learning algorithm. The framework described herein is therefore applicable and adaptable to a number of real world service provider systems and applications, such as, for example, detecting network performance degradation in a service provider network or detecting anomalous conditions from data received from a sensor while filtering out false positives.