Log Content Model for Memory-Constrained Anomaly Detection
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Solution Overview
Problem
Existing log analysis methods for detecting anomalies in computer systems, especially in devices with limited memory, often result in high false positive rates, making them impractical for real-world deployment, particularly in IoT systems where memory constraints are significant and log standards are not well established.
Innovation Solution
A computer-implemented method and system for content-level anomaly detection that generates a log content model based on training logs, transforms it into a modified model to limit memory usage, and performs anomaly detection on testing logs using membership-testing, range checking, and null models, allowing for efficient anomaly identification while accommodating devices with limited memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing log analysis methods are used for anomaly detection, then detection capability is provided, but false positive rates are high and memory usage is excessive
Solution Approach 1:
The patent segments the anomaly detection process into distinct phases: training phase for building content models from logs, and detection phase for comparing new logs against these models. This segmentation allows the system to learn normal patterns during training and then reliably detect deviations during detection, reducing false positives by separating model building from model application.
Solution Approach 2:
The patent changes parameters by transforming raw log content into structured content models with specific parameters (frequency thresholds, pattern templates, statistical baselines). These parameterized models enable more precise anomaly detection by comparing new logs against quantified normal behavior patterns rather than raw unstructured logs, improving detection accuracy while reducing false positives.
2Measurement precision
If detailed log content modeling is performed for accurate anomaly detection, then detection precision is improved, but memory usage increases
Solution Approach 1:
The patent extracts only the essential content features from logs that are relevant for anomaly detection, storing them in compact content models. Instead of retaining complete log data, the system extracts key parameters such as frequency patterns, content templates, and statistical measures. This extraction maintains detection precision while significantly reducing the memory footprint by keeping only the necessary diagnostic information.
Solution Approach 2:
The patent applies partial action by implementing selective content modeling that focuses on the most discriminative log features for anomaly detection. The system processes and models only the critical content aspects needed for detection rather than analyzing every log detail, achieving sufficient precision with reduced memory consumption by applying the principle of doing enough rather than everything.
Data Source
AI summary
Systems and methods for implementing content-level anomaly detection for devices having limited memory are provided. At least one log content model is generated based on training log content of training logs obtained from one or more sources associated with the computer system. The at least one log content model is transformed into at least one modified log content model to limit memory usage. Anomaly detection is performed for testing log content of testing logs obtained from one or more sources associated with the computer system based on the at least one modified log content model. In response to the anomaly detection identifying one or more anomalies associated with the testing log content, the one or more anomalies are output.