Self-Supervised Anomaly Detection in Event Logs
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Solution Overview
Problem
Existing systems face challenges in efficiently detecting and remediating anomalous events in complex computer and robotic systems due to the time-consuming analysis of vast event logs, which can hinder system operation and maintenance.
Innovation Solution
A self-supervised learning system employing natural language processing and neural networks analyzes event logs to identify anomalies independently, treating events as words and predicting sequences to detect low-probability or unexpected patterns, triggering automated remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard logging mechanisms are used to record events with timestamps and system state information, then event data is captured for failure diagnostics, but the time required to analyze the logs increases significantly
Solution Approach 1:
The system performs preliminary actions by training the anomaly detection model offline using historical event logs before deployment. The model learns normal system behavior patterns in advance, enabling real-time anomaly detection without requiring time-consuming analysis of new logs. This preliminary training phase separates the heavy computational work from the operational phase, resolving the time contradiction.
Solution Approach 2:
The system creates a computational model (copy) of normal system behavior through offline training. Instead of analyzing actual event logs in real-time, the system uses the trained model to detect anomalies by comparing current events against the learned normal patterns. This copying approach replaces time-consuming log analysis with faster model inference.
2Measurement precision
If manual analysis of event logs is performed to identify anomalous conditions, then accurate error identification is achieved, but system operation and maintenance are hindered due to time consumption
Solution Approach 1:
The system implements self-service by automatically detecting anomalies and generating remediation recommendations without requiring manual human analysis. The anomaly detection model continuously monitors system events and autonomously identifies problematic conditions, freeing maintenance personnel from time-consuming manual log analysis while maintaining high detection accuracy through learned patterns.
Solution Approach 2:
The system replaces the mechanical process of manual log analysis with an automated computational model. Instead of human experts manually examining event logs to identify anomalies, the trained machine learning model performs the analysis automatically, substituting human cognitive work with computational processing that is both faster and scalable.
3Measurement precision
If comprehensive event logging is implemented to capture all system events for anomaly detection, then detection accuracy is improved, but the complexity of processing and analyzing the logs increases
Solution Approach 1:
The system extracts and focuses on the most relevant features from comprehensive event logs during the offline training phase. The anomaly detection model learns to identify and weight important event characteristics while ignoring redundant information. This extraction process reduces processing complexity by concentrating on key diagnostic features rather than analyzing all log data in detail during operational anomaly detection.
Solution Approach 2:
The system transforms comprehensive event log data into a simplified representation through the trained model. During offline training, the model learns optimal parameter transformations that capture essential anomaly patterns while reducing data dimensionality. This parameter transformation enables accurate anomaly detection with reduced processing complexity by working with compressed, meaningful features rather than raw comprehensive logs.
Data Source
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AI summary
Systems, apparatuses and methods may provide for technology that identifies a sequence of events associated with a computer architecture, categorizes, with a natural language processing system, the sequence of events into a sequence of words, identifying an anomaly based on the sequence of words and triggering an automatic remediation process in response to an identification of the anomaly.