System Log Anomaly Detection and Remediation Through Contextual Tuning
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Solution Overview
Problem
Existing systems face challenges in accurately identifying and addressing anomalies in system logs due to human error and inefficiencies in manual log analysis, particularly in large datasets, leading to potential issues being overlooked in IT asset performance testing.
Innovation Solution
A machine learning-based approach utilizing a Large Language Model (LLM) is fine-tuned through Contextual Contrastive Learning and Few-Shot sample generation to enhance anomaly detection and reasoning, leveraging historical logs to understand syntax and context for improved anomaly identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual log analysis is used, then human error and inefficiencies occur, but the system complexity remains low
Solution Approach 1:
The patent replaces manual log analysis with a machine learning-based system that uses clustering algorithms and anomaly detection models to automatically identify anomalies in system logs. This substitution eliminates human error and inefficiencies while maintaining manageable system complexity through automated processing mechanisms.
Solution Approach 2:
The system performs self-service by automatically analyzing system logs, identifying anomalies, and generating remediation actions without requiring manual intervention. The machine learning model continuously learns from log data to improve its anomaly detection capabilities, enabling the system to serve itself in the anomaly detection process.
2Productivity
If automated anomaly detection is implemented, then productivity increases, but the complexity of the system increases
Solution Approach 1:
The patent segments the log analysis process into distinct components: data collection, clustering, anomaly detection, and remediation action generation. This segmentation allows each component to be optimized independently, improving overall productivity while keeping individual module complexities manageable through modular architecture.
Solution Approach 2:
The system introduces an intermediary machine learning model that mediates between raw log data and anomaly detection. This intermediary layer processes and pre-processes log information, making the anomaly detection more efficient and reducing the complexity of direct analysis by breaking down the processing pipeline into manageable stages.
3Measurement precision
If contextual contrastive tuning is applied, then anomaly detection precision improves, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing log data into clusters and preparing contextual information before anomaly detection. This pre-processing step organizes data in advance, allowing the machine learning model to focus only on identifying anomalies within pre-organized data structures, thereby improving precision without proportionally increasing processing time.
Solution Approach 2:
The system applies local quality by analyzing anomalies within specific contextual clusters rather than uniformly across all log data. This localized approach allows the machine learning model to concentrate computational resources on relevant data segments, improving detection precision for specific anomaly types while reducing overall processing time through targeted analysis.
Data Source
AI summary
An apparatus comprises at least one processing device configured to generate a first data structure comprising a numerical representation of content of a given system log associated with an information technology asset, to utilize the first data structure to determine a system log cluster to which the given system log belongs, to select non-anomalous system logs which are part of the system log cluster, and to perform contextual contrastive tuning of a machine learning model utilizing the selected non-anomalous system logs. The at least one processing device is further configured to generate a second data structure utilizing the tuned machine learning model which takes as input the first data structure, the second data structure characterizing (i) detected anomalies and (ii) causes of the detected anomalies. The at least one processing device is further configured to perform remediation actions, selected based on the second data structure, for the information technology asset.