Unsupervised Log Anomaly Detection Using LSTM Autoencoders
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Solution Overview
Problem
Current systems for detecting anomalies in system log data struggle to effectively identify abnormalities, especially in large volumes of unstandardized and customized log entries, often missing hidden features and anomalies due to lack of standardized analysis methods.
Innovation Solution
The method involves pre-processing log data entries, identifying sequences, generating key performance indicators (KPIs), training machine learning models using these KPIs, and employing multiple LSTM models with autoencoders, dropout, and uncertainty estimation to detect anomalies based on hidden features and meta-data dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional anomaly detection methods are used on large volumes of unstandardized log data, then processing speed is maintained, but detection accuracy and ability to identify hidden features deteriorate
Solution Approach 1:
The patent segments log data into standardized templates and patterns, dividing the complex analysis task into manageable components. Log entries are segmented into structured formats with identified sequences, allowing systematic analysis of individual components rather than overwhelming raw data processing.
Solution Approach 2:
The patent introduces meta-data dimensions beyond traditional log analysis, incorporating temporal patterns, frequency analysis, and sequence relationships. This multi-dimensional approach transforms standardization from a constraint into an enrichment mechanism, adding layers of analysis that reveal hidden features and anomalies.
2Reliability
If multiple machine learning models are used for anomaly detection, then detection reliability is improved, but computational resources and processing time increase
Solution Approach 1:
The patent employs multiple LSTM models with different specialized functions (autoencoder, dropout, uncertainty estimation) rather than relying on a single comprehensive model. Each model performs a specific aspect of anomaly detection, and their results are combined through majority voting, achieving high reliability while managing computational resources efficiently.
Solution Approach 2:
The system uses unsupervised learning approaches where models learn normal patterns from data without requiring labeled anomaly examples. The autoencoder models learn to reconstruct normal log sequences and automatically identify anomalies as deviations, reducing the need for extensive supervised training data and computational overhead for model development.
3Stability of the object's composition
If log data is standardized for analysis, then detection consistency is improved, but information about customized log features is lost
Solution Approach 1:
The patent creates a universal standardized template structure that can accommodate diverse log formats and customized features. The standardization framework is designed to be multi-functional, capturing both common log elements and application-specific customized features within a unified analysis structure, preventing information loss while ensuring consistency.
Solution Approach 2:
The patent nests standardized templates within customized log structures, allowing hierarchical representation where general patterns are captured at higher levels while application-specific features are preserved at lower levels. This nested structure maintains both standardization for consistent analysis and preservation of customized features for domain-specific insights.
Data Source
AI summary
Detecting log data anomalies by pre-processing log data entries, identifying sequences of the log data entries, generating at least one key performance indicator (KPI) for the log data entries according to the sequences, training at least one machine learning (ML) model according to the KPI, and identifying a log data entry anomaly according to the machine learning model.