NLP Log Parsing via Standardized Format Conversion
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Solution Overview
Problem
Existing techniques for parsing log records from various sources are time-consuming and resource-intensive due to format and data content differences, requiring tailored parsing solutions and manual oversight, making them inefficient for analyzing diverse log records.
Innovation Solution
A method and system for training a language processing model that converts log records into a standardized format using a conversion rule set, enabling the model to identify items and relationships in log records and providing probabilistic assessments, which can be used for threat detection and log searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tailored parsing solutions are built for each specific source or source type, then parsing accuracy is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent applies universality by developing a single NLP-based parsing system that can handle multiple log sources and formats through a unified conversion rule set, eliminating the need for separate tailored parsing solutions for each source type while maintaining high parsing accuracy across diverse log formats
Solution Approach 2:
The patent changes the approach from static, source-specific parsing rules to dynamic NLP-based parsing that uses conversion rule sets which can adapt to different log formats. The system transforms log records through parameterized conversion rules rather than hardcoding specific parsing logic for each source
2Reliability
If manual oversight is performed for parsing solutions, then parsing reliability is improved, but loss of time and resource consumption increase
Solution Approach 1:
The patent implements self-service by enabling the NLP model to automatically parse and convert log records independently without requiring continuous manual oversight. The conversion rule set allows the system to self-correct and adapt to different log formats autonomously, maintaining reliability while eliminating manual intervention time
Solution Approach 2:
The patent incorporates feedback mechanisms where the NLP model processes log records and the system can learn from parsing results to improve future parsing operations. This feedback loop enables the system to maintain high reliability through continuous improvement without requiring manual review of each parsing operation
3Ease of operation
If existing parsing techniques are used for diverse log records, then ease of operation is maintained, but productivity decreases due to time consumption
Solution Approach 1:
The patent replaces the mechanical, rule-based parsing system with an NLP-based system that uses natural language processing techniques. This substitution maintains ease of operation through a unified interface while dramatically improving productivity by eliminating the need for manual parsing configuration and reducing processing time through automated NLP inference
Data Source
AI summary
Methods and systems for training a language processing model. The methods may involve receiving a first log record in a first format, wherein the first log record includes annotations describing items in the first log record, and then creating a second log record in a second format comprising data from the first log record utilizing the annotations in the first log record and a conversion rule set. The second log record may then be used to train a language processing model so that a trained model can identify items in a third log record and the relationships therebetween.