Computer Log Canonicalization Using a Cybersecurity LLM
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Solution Overview
Problem
Existing systems struggle to efficiently convert computer system logs into a human-readable format for analysis, leading to complexity in diagnosing issues, understanding user behavior, and identifying security threats.
Innovation Solution
The system employs a method to canonicalize computer system logs into natural language processed representations using a cyber security purpose-based large language model. This involves receiving log files, applying natural language processing, generating plain English translations, and converting them into multi-dimensional vectors for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If computer system logs are processed in their original technical format, then data analysis can be performed, but the complexity of diagnosing issues and understanding user behavior increases
Solution Approach 1:
The patent introduces a large language model as an intermediary between the raw log data and the analyst. The LLM translates complex technical log formats into simplified natural language explanations, acting as a mediator that bridges the gap between raw data and human understanding without requiring analysts to directly interpret complex log structures.
Solution Approach 2:
The patent changes the representation parameters of the log data by transforming it from raw technical formats into natural language descriptions. This parameter transformation makes the data more accessible and easier to analyze while preserving the essential information needed for diagnosis and understanding.
2Productivity
If logs are converted to natural language representations, then human readability and analysis efficiency improve, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and indexing log data into natural language representations before actual analysis is needed. The system can pre-generate summaries and translations of log patterns, making them ready for quick retrieval and analysis when issues arise, thereby reducing real-time processing requirements.
3Measurement precision
If a large language model is used to translate and canonicalize logs, then the accuracy of data representation improves, but the computational resources and energy consumption increase
Solution Approach 1:
The patent applies partial action by using the large language model selectively rather than universally. The system can determine when LLM translation is necessary based on the complexity of the log entry or the specific analysis task, applying the computationally intensive translation only when needed rather than processing all logs through the LLM, thereby reducing overall energy consumption while maintaining high accuracy where required.
Data Source
AI summary
Provided herein is an exemplary system for canonicalizing computer system logs into natural language processed representations for data analysis, the system including a real-time data collector, a cyber security purpose-based large language model communicatively coupled to the real-time data collector, a multi-dimensional vector generator communicatively coupled to the cyber security purpose-based large language model and a vectorization index and a prediction engine communicatively coupled to the multi-dimensional vector generator.