Adaptive Anomaly Detection via Regression and Statistical Models
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Solution Overview
Problem
Conventional monitoring systems for detecting behavioral anomalies in applications require manual definition of alert thresholds, leading to labor-intensive processes and potential false positives, which can fail to detect anomalies or generate unnecessary alerts.
Innovation Solution
A method that retrieves historical usage information, generates a regression model to predict usage behavior, and a statistical model to identify outliers, allowing for real-time anomaly detection without manual intervention and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual alert thresholds are defined for key application metrics, then the system can detect anomalies, but the process becomes labor intensive and may generate false positives
Solution Approach 1:
The system automatically generates and updates alert thresholds based on historical usage data without requiring manual intervention. The machine learning model analyzes past behavior patterns and autonomously determines appropriate threshold values, allowing the system to self-configure and adapt to changing conditions over time.
Solution Approach 2:
The system dynamically adjusts alert thresholds as a function of historical usage patterns rather than using fixed manual values. The machine learning model continuously refines threshold parameters based on actual application behavior, enabling adaptive detection that responds to seasonal variations and workload changes.
2Device complexity
If fixed alert thresholds are set for key metrics, then anomaly detection is simplified, but the system fails to detect abnormal behavior when metrics fall outside predefined intervals
Solution Approach 1:
The system transitions from static fixed thresholds to dynamic adaptive thresholds that automatically adjust based on historical usage patterns. The machine learning model continuously learns from actual application behavior and updates thresholds accordingly, enabling detection of anomalies that would fall outside rigid predefined intervals while maintaining reasonable system complexity.
3Reliability
If alert thresholds are designed to trigger on any metric deviation, then anomaly detection sensitivity is increased, but false positives increase and authorized user access may be blocked
Solution Approach 1:
The system incorporates feedback mechanisms where actual usage data is continuously fed back into the machine learning model to refine threshold calculations. This feedback loop allows the system to learn from past detections and adjust thresholds to better distinguish between genuine anomalies and normal variations in authorized usage patterns.
Solution Approach 2:
The system performs preliminary analysis of historical usage data to establish baseline patterns before detecting current anomalies. By pre-learning normal behavior patterns through machine learning, the system can more accurately distinguish between authorized variations and genuine malicious activity, reducing false positives before alerts are generated.
Data Source
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AI summary
Disclosed are systems and methods of detecting a behavioral anomaly in an application. In one example, a method may comprise retrieving and identifying at least one key metric from historical usage information for an application on a computing device. The method may comprise generating a regression model configured to predict usage behavior associated with the application and generating a statistical model configured to identify outliers in the data associated with the at least one key metric. The method may comprise receiving usage information in real-time for the application. The method may comprise predicting, using the regression model, a usage pattern for the application indicating expected values of the at least one key metric. In response to determining that the usage information does not correspond to the predicted usage pattern and does not comprise a known outlier, the method may comprise detecting the behavioral anomaly.