Behavior Baseline Monitoring for ML Rule Set Anomaly Detection
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Solution Overview
Problem
Machine-learned rule sets in production environments often fail to perform well due to differences between training and production data, making it difficult to detect anomalous behavior and ensuring the reliability of system outputs, as these systems lack intent and motivation and are not handcrafted based on human observation.
Innovation Solution
Establishing a behavior baseline during the training process by characterizing the output of rule sets and comparing it with production data to identify anomalies, allowing for remediation and prevention of future anomalies by monitoring and adjusting the rule sets' behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-learned rule sets are deployed in production environments, then automation and productivity are improved, but reliability deteriorates due to differences between training and production data
Solution Approach 1:
The patent establishes a behavior baseline during the training process by characterizing the output of rule sets before deployment. This preliminary characterization includes recording input data, outputs, and intermediate values to create an expected behavior profile that will be used later to detect anomalies in production environments.
Solution Approach 2:
The patent implements a feedback mechanism where production outputs are continuously compared against the pre-established behavior baseline. When deviations are detected, the system generates alerts and can trigger remediation actions, creating a closed-loop control system that monitors and corrects anomalous behavior in machine-learned rule sets.
2Difficulty of detecting and measuring
If manually-crafted rules are used to detect anomalous human behavior, then detection capability is improved, but device complexity and time consumption increase
Solution Approach 1:
Instead of creating complex manual rules for every possible anomaly, the patent creates a simplified copy or representation of normal behavior through the behavior baseline. This baseline captures the essential characteristics of expected operation without encoding complex conditional logic, making the detection system simpler while maintaining high detection capability.
Solution Approach 2:
The patent inverts the traditional approach by not trying to define what anomalies are, but rather by defining what normal behavior is. The behavior baseline represents normal operation, and anything deviating from this baseline is automatically flagged as anomalous, simplifying the detection logic.
3Ease of operation
If machine-learned rule sets operate without behavior baselines, then ease of operation is improved, but loss of information about anomalous behavior increases
Solution Approach 1:
The behavior baseline is established in advance during the training phase, capturing essential information about expected system behavior including input-output relationships and intermediate values. This preliminary information capture enables later anomaly detection without complicating the operational phase.
Data Source
AI summary
Roughly described, anomalous behavior of a machine-learned computer-implemented individual can be detected while operating in a production environment. A population of individuals is represented in a computer storage medium, each individual identifying actions to assert in dependence upon input data. As part of machine learning, the individuals are tested against samples of training data and the actions they assert are recorded in a behavior repository. The behavior of an individual is characterized from the observations recorded during training. In a production environment, the individuals are operated by applying production input data, and the production behavior of the individual is observed and compared to the behavior of the individual represented in the behavior repository. A determination is made from the comparison of whether the individual's production behavior during operation is anomalous.