Log Anomaly Detection via Synthetic Data and Hybrid Classification
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Solution Overview
Problem
Modern anomaly detection systems in initial deployment situations face inefficiencies due to scarce training data, leading to high levels of false positives and increased computational wastage, especially in "0-day" scenarios where log data is scarce and mixed with healthy and unhealthy logs.
Innovation Solution
A computer-implemented method that classifies log lines using a dictionary-based classifier, clusters templatized log lines, and identifies anomalies based on factors like log maturity and ratio of erroneous to non-erroneous log lines, with human-in-the-loop validation and incremental system updates to reduce false positives and require less training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection systems are deployed in initial situations, then they can detect anomalies, but they produce high levels of false positives due to scarce training data
Solution Approach 1:
The system performs self-service by automatically generating synthetic training data from operational logs and using it to train its own anomaly detection models without requiring external labeled datasets. The system iteratively improves its detection capability by consuming its own operational data and refining its models through continuous learning cycles.
Solution Approach 2:
The system performs preliminary action by pre-processing operational logs to identify patterns and generate synthetic training data before formal anomaly detection begins. It creates templatized representations of log entries and pre-trains models using these synthesized datasets, preparing the system for more accurate anomaly detection from the outset.
2Measurement precision
If more training data is collected to improve anomaly detection, then detection accuracy improves, but computational costs and data storage requirements increase
Solution Approach 1:
The system creates synthetic copies of operational log data through templatization and generation processes. Instead of collecting and processing vast amounts of real labeled anomaly data, the system generates artificial training samples that replicate the statistical properties and patterns of real logs, enabling effective model training with reduced computational overhead.
Solution Approach 2:
The system segments the anomaly detection process into distinct phases: synthetic data generation, model pre-training on generated data, and fine-tuning on limited real data. This segmentation allows the system to efficiently process data at different stages, reducing overall computational requirements while maintaining detection accuracy.
3Measurement precision
If manual validation of log lines is performed to reduce false positives, then detection precision improves, but processing time and operational overhead increase
Solution Approach 1:
The system implements feedback loops where validation results from reliability engineers are fed back into the training process. The system learns from human validations and automatically adjusts its detection thresholds and model parameters, progressively reducing the need for manual validation while improving precision over time.
Solution Approach 2:
The system applies partial manual validation only to uncertain cases that fall within a confidence threshold, rather than validating all log lines. This selective approach maintains high precision for ambiguous cases while allowing the system to process clear-cut anomalies automatically, reducing overall processing time and operational overhead.
Data Source
AI summary
One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line; templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines; cluster erroneous log templates into erroneous log template clusters and non-erroneous log templates into non-erroneous log template clusters; identify one or more log lines as anomalous utilizing a plurality of factors including a log maturity, a number of encountered log template clusters, and a ratio of classified erroneous log lines to classified non-erroneous log lines; responsive to one or more identified anomalous log lines, validate the identified anomalous log lines utilizing a site reliability engineer and human-in-the-loop validation; train a log anomaly model utilizing one or more validated log lines; and identify a subsequent log line as anomalous utilizing the trained log anomaly model.