Predictive Alert Classification via Machine Learning
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Solution Overview
Problem
IT service providers face challenges in quickly and accurately classifying alerts as actionable or non-actionable, especially when faced with a high volume of alerts, which can lead to delays in detecting and resolving service and customer impacting incidents.
Innovation Solution
A predictive machine learning model is trained to classify alerts as actionable or non-actionable using data fields from alert records, providing a probability for each alert, and presenting them in a network monitoring user interface based on this classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of alerts is performed by IT personnel, then accuracy in determining actionable alerts can be maintained, but the time required and workload increase significantly when faced with high volumes of alerts
Solution Approach 1:
A machine learning model is introduced as an intermediary between alert generation and human analysis. The model automatically classifies alerts as actionable or non-actionable based on historical data and patterns, providing predictions that guide human personnel's attention to only the most critical alerts, thus reducing time loss while maintaining classification accuracy
Solution Approach 2:
The system performs preliminary classification of alerts using machine learning before human personnel review them. By pre-processing and categorizing alerts in advance, the system reduces the time required for human analysis while maintaining accurate identification of actionable items through the model's trained patterns
2Reliability
If all alerts are presented to IT personnel for review, then none are missed, but the information overload makes it difficult to quickly identify actionable ones
Solution Approach 1:
The system applies different presentation qualities to different alerts based on their predicted actionability. Actionable alerts are highlighted, prioritized, or presented with special visual indicators in the user interface, while non-actionable alerts are downgraded or grouped separately. This local differentiation makes it easy for personnel to quickly identify and focus on the critical few actionable alerts among many non-actionable ones, maintaining detection completeness while dramatically improving review efficiency
3Productivity
If a machine learning model is used to automatically classify alerts, then processing time is reduced, but the complexity of the system increases
Solution Approach 1:
The machine learning model is trained on historical alert data to automatically learn patterns and characteristics of actionable versus non-actionable alerts. Once trained, the model serves itself by continuously classifying new alerts without requiring manual configuration or intervention for each alert. This self-service capability increases processing throughput while managing system complexity through automated learning rather than manual rule management
Data Source
AI summary
An approach is provided for providing predictive classification of actionable network alerts. The approach includes receiving the plurality of alerts. Each alert of the plurality of alerts indicates an alarm condition occurring at a monitored network system, and is a data record comprising one or more data fields describing the alarm condition. The approach also includes classifying said each alert using a predictive machine learning model. The predictive machine learning model is trained to classify said each alert as actionable or non-actionable using the one or more data fields of said each alert as one or more respective classification features, and to calculate a respective probability that said each alert is actionable or non-actionable. The approach further includes presenting the plurality of alerts in a network monitoring user interface based on the respective probability of said each alert.