Multi-Model Log Anomaly Detection for Non-Homogeneous Patterns
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Solution Overview
Problem
Existing deep neural networks for anomaly detection in system logs are trained using homogenous data, leading to failure in identifying non-represented anomalies, which can cause errors and waste computing resources.
Innovation Solution
An anomaly detection system trains multiple sets of models using different types of historical log data to create a model matrix that can identify non-homogenous input logs, improving error detection and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single deep neural network is trained using homogenous data, then the model structure remains simple and training is efficient, but the system fails to identify non-represented anomalies
Solution Approach 1:
The system divides the anomaly detection task into multiple specialized models, each trained on different types of historical log data (e.g., security logs, system logs, application logs). This segmentation allows each model to become an expert in detecting specific anomaly patterns, thereby improving overall detection reliability without requiring a single overly complex model to learn all patterns simultaneously.
Solution Approach 2:
The system combines multiple neural network models with different training data compositions into a unified anomaly detection system. Each model acts as a component with specialized knowledge from its specific training domain, and their collective output forms a composite detection capability that covers a broader range of anomaly types than any single model could achieve alone.
2Adaptability or versatility
If multiple models are trained using diverse historical log data, then the ability to detect non-represented anomalies improves, but the training process and system complexity increase
Solution Approach 1:
The system designs each neural network model to be a universal anomaly detector within its specific domain. Each model is trained on a particular type of historical log data but is configured to detect various anomaly patterns relevant to that data type. This multi-functionality within each model, combined with the collective coverage across models, achieves broad anomaly type coverage without requiring excessive complexity in any single component.
Solution Approach 2:
The diverse historical log data is segmented into distinct training sets for different models, with each model specializing in specific log types. This segmentation strategy allows the system to manage complexity by organizing models according to their data domains, making the overall system more tractable than a single model attempting to process all diverse data types simultaneously.
3Productivity
If homogenous training data is used, then training efficiency is maintained, but errors in anomaly detection increase and computing resources are wasted
Solution Approach 1:
Each neural network model is trained on locally optimized homogeneous data specific to its domain (e.g., security logs for security models, system logs for system models). This local quality approach allows each model to train efficiently on relevant data while maintaining high detection accuracy for its specific anomaly types, avoiding the waste of training on irrelevant data types that would occur in a single homogenous model.
Solution Approach 2:
The system trains multiple models with partial overlap in their detection capabilities rather than attempting to create a single comprehensive model. This partial action approach allows parallel training of specialized models that can be developed and optimized independently, improving training efficiency while the collective coverage prevents computing resource waste from missed anomalies.
Data Source
AI summary
An anomaly detection system may train a first model associated with detecting anomalies involving a log source based on first historical logs associated with the log source and may train a second model associated with the log source based on second historical logs associated with the log source and target data that are associated with the second historical logs. The anomaly detection system may cause the first model to process the second historical logs to generate training anomaly data of the log source. The anomaly detection system may train a third model associated with the log source based on the training anomaly data and the target data. The anomaly detection system may configure, based on outputs from the second model and the third model, an anomaly detection model to detect the anomalies in input data that are provided to the first model and the second model.