Context-Based Anomaly Detection for Lower False Positives
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Solution Overview
Problem
Existing anomaly detection methods suffer from high false positive rates, particularly when predicting anomalies in IT infrastructure metrics, due to the lack of context-based analysis.
Innovation Solution
Implementing a multi-layered context-based anomaly detection system that utilizes context-based bounds, integrating knowledge of the metric name, model selection, and historical data distribution to refine anomaly prediction scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods are used, then anomaly detection can be performed, but false positive rates are high
Solution Approach 1:
The patent segments the anomaly detection process into multiple independent components: context extraction module, anomaly detection module, and validation module. Each component handles a specific aspect of the detection process, allowing for more precise control over false positive generation and enabling targeted refinement of detection accuracy without compromising overall reliability
Solution Approach 2:
The patent introduces context information as an intermediary element between raw data and anomaly detection results. This context layer acts as a mediator that provides additional semantic understanding, allowing the system to distinguish between genuine anomalies and normal variations, thereby reducing false positives while maintaining detection accuracy
2Reliability
If context-based analysis is added to improve detection accuracy, then false positives are reduced, but system complexity increases
Solution Approach 1:
The patent implements a universal context extraction framework that can handle multiple types of context information (historical data, metadata, environmental factors) through a single standardized interface. This multi-functional approach allows the system to reduce false positives across different anomaly types without requiring separate complex processing paths for each context type
Solution Approach 2:
The patent performs context extraction and preparation as a preliminary action before the actual anomaly detection process. By pre-processing and organizing context information in advance, the system reduces the computational burden during real-time detection, thereby lowering operational complexity while maintaining the reliability benefits of context-based analysis
Data Source
AI summary
Time-series data is received. Using an identifier of the time-series data, contextual reference anomaly detection parameters are identified from a repository. A data trend of the time-series data is classified. Based on the classified data trend, a type of model to be generated for the time-series data is selected and a model having generated anomaly detection parameters is generated. A history of anomaly detection parameters determined for the time-series data is identified, and the generated anomaly detection parameters are adjusted based on the contextual reference anomaly detection parameters and the history of anomaly detection parameters.