Anomaly Detection Feedback Loop for Alert Noise Reduction
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Solution Overview
Problem
Existing anomaly detection systems generate a large number of statistical anomalies, requiring significant manual effort for end-users to identify relevant ones, as they are not specifically tailored to the user's application, leading to unnecessary alerts and inefficiency.
Innovation Solution
Anomaly detection systems that utilize feedback mechanisms to adjust sensitivity and relevance, allowing users to classify anomalies based on their specific needs, thereby learning to distinguish between relevant and irrelevant anomalies, and modifying the threshold for anomaly classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anomaly detection systems detect all statistical anomalies, then detection completeness is improved, but manual effort and alert noise increase
Solution Approach 1:
The system implements a feedback loop where user interactions with anomaly alerts (acknowledging, dismissing, or marking as relevant) are captured and used to retrain the machine learning model. This feedback mechanism enables the system to learn from user behavior patterns and improve its anomaly filtering capabilities over time, automatically reducing manual effort while maintaining detection completeness.
Solution Approach 2:
The system performs self-improvement by automatically retraining its machine learning models using feedback data without requiring manual intervention. The anomaly detection system serves itself by autonomously adjusting its parameters and thresholds based on accumulated user feedback, thereby reducing the need for continuous manual configuration and effort.
2Device complexity
If anomaly detection systems use default sensitivity settings, then system simplicity is improved, but relevance to specific applications deteriorates
Solution Approach 1:
The system dynamically adjusts its sensitivity and detection thresholds based on feedback from user interactions. Rather than using fixed default settings, the machine learning model continuously adapts its parameters to match the specific application context and user preferences, enabling the system to maintain simplicity while achieving high application relevance through automatic adaptation.
Solution Approach 2:
The system automatically modifies detection parameters such as sensitivity thresholds and anomaly criteria based on accumulated feedback data. By changing these parameters dynamically rather than requiring manual configuration for each application, the system maintains operational simplicity while achieving tailored relevance to specific use cases.
3Reliability
If anomaly detection systems generate comprehensive anomaly lists, then detection coverage is improved, but information overload increases
Solution Approach 1:
The system extracts and prioritizes only the most relevant anomalies from the comprehensive detection list based on user feedback and application-specific criteria. By filtering out irrelevant statistical anomalies and presenting only the subset that matters to the user, the system maintains full detection coverage while eliminating information overload and improving the signal-to-noise ratio.
Solution Approach 2:
The system applies different filtering and prioritization criteria to different types of anomalies based on their relevance to specific applications. Rather than treating all anomalies uniformly, the system tailors its output quality to match local requirements, presenting detailed information for high-priority anomalies while summarizing or omitting low-priority ones, thereby maintaining coverage without overwhelming the user.
Data Source
AI summary
Techniques for performing anomaly detection are described. An exemplary method includes receiving a request to detect potential anomalies using an anomaly detection system having at least one anomaly scoring model; processing the received data using the anomaly detection system to score the data to determine when the data is potentially anomalous based on one or more thresholds; requesting feedback of at least one determined potential anomaly; receiving feedback on the least one determined potential anomaly; and adjusting at least one of one or more of thresholds used to determine potential anomalies and what is considered an anomaly without adjusting the at least one anomaly scoring model.