Log Data Anomaly Detection via Baseline Signature Profiles
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Solution Overview
Problem
Analyzing vast amounts of log data from computer networks for IT security, operations, and compliance is difficult and expensive due to its voluminous nature and varied formats, requiring significant expertise and effort from analysts to identify important events.
Innovation Solution
A data collection and analysis platform that automates data ingestion, parsing, and anomaly detection, using a scalable architecture with distributed components, automatic parser selection and generation, and real-time analysis to identify potentially critical events without requiring manual queries or thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of log data is performed by expert analysts, then detection precision of important events is improved, but productivity is worsened due to the vast quantity of data and time required for analysis
Solution Approach 1:
The system performs automatic anomaly detection without requiring manual analyst intervention. The anomaly detection engine autonomously analyzes log data, identifies patterns, and generates alerts for important events, enabling the system to serve itself rather than relying on external expert resources.
Solution Approach 2:
The patent replaces the mechanical process of manual log analysis by human analysts with an automated computational system. The anomaly detection engine uses algorithms and machine learning techniques to substitute human cognitive processes, achieving both high detection precision and improved productivity through automation.
2Productivity
If automated anomaly detection is implemented, then productivity is improved by reducing manual analysis requirements, but device complexity is worsened due to the sophisticated detection algorithms and system architecture
Solution Approach 1:
The system is divided into distinct modular components: log data reception module, anomaly detection engine, and alert generation module. This segmentation allows each component to be developed, maintained, and scaled independently, managing overall system complexity while maintaining high productivity through automated operations.
Solution Approach 2:
The anomaly detection engine is designed as a universal system capable of analyzing multiple types of log data from various sources simultaneously. By creating a multi-functional detection mechanism that handles diverse data formats and anomaly types, the system achieves high productivity without proportionally increasing complexity through specialized components for each data type.
3Measurement precision
If comprehensive log data capture is performed across all network devices, then detection precision is improved by having more data to analyze, but loss of information is worsened due to the voluminous nature of data that cannot be fully processed
Solution Approach 1:
The anomaly detection engine extracts only the most relevant and significant information from voluminous log data. By identifying and extracting key anomaly indicators rather than attempting to process all data equally, the system maintains high detection precision while preventing information loss through selective focus on critical patterns and events.
Solution Approach 2:
The system transforms raw log data into standardized parameters and features that can be efficiently processed. By changing the parameter representation of data (e.g., converting diverse log formats into uniform anomaly scores and patterns), the system achieves comprehensive analysis without being overwhelmed by data volume, maintaining precision while managing information retention.
Data Source
AI summary
Analyzing log data, such as security log data and machine data, is disclosed. A baseline is built for a set of machine data. The baseline is built at least in part by determining a plurality of signature profiles for a plurality of respective time slices. An occurrence of an anomaly associated with the source of the machine data is determined. The occurrence is determined at least in part by determining that received machine data does not conform to the baseline within a threshold.