AI Log Classification for Faster Security Data Normalization
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Solution Overview
Problem
Existing cybersecurity platforms face inefficiencies and vulnerabilities due to the complexity of analyzing and reconciling diverse cybersecurity data from various tools, leading to inadequate protection against sophisticated threats.
Innovation Solution
Implementing a security log type classification system using an artificial intelligence (AI) model to automatically identify and label log types, improving accuracy and efficiency in log data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity measures and multiple tools are used, then comprehensive protection against threats is achieved, but the complexity of analyzing and reconciling diverse cybersecurity data increases
Solution Approach 1:
The patent segments the log data into standardized fields (timestamp, host, process, user, message) and categories (authentication, authorization, audit, error, information). This segmentation allows diverse log formats from multiple security tools to be reconciled into a unified structure, reducing analysis complexity while maintaining comprehensive protection capabilities.
Solution Approach 2:
The system creates a universal log analysis platform that handles multiple log types and formats through a single standardized framework. The normalized log structure serves multiple functions: parsing diverse inputs, categorizing security events, and providing consistent output for security operations, thereby reducing the need for separate analysis tools.
2Adaptability or versatility
If multiple cybersecurity tools generate large amounts of data, then comprehensive security coverage is improved, but the volume and diversity of data makes analysis cumbersome
Solution Approach 1:
The system performs preliminary normalization of log data during the parsing stage, transforming diverse log formats into a standardized structure before analysis. This preliminary action reduces the complexity of subsequent analysis operations, allowing security teams to efficiently process large volumes of data from multiple tools without encountering format-related obstacles.
Solution Approach 2:
The patent changes the parameter representation of log data by normalizing field names, data types, and formats into a consistent schema. This parameter standardization transforms diverse, hard-to-analyze data into a uniform structure that can be efficiently processed by security operations tools and human analysts, improving productivity while maintaining comprehensive coverage.
3Measurement precision
If manual log classification is performed, then accuracy can be controlled, but time consumption and labor requirements increase
Solution Approach 1:
The system implements self-service log classification through automated parsing and categorization based on normalized log structures. The automated system independently identifies log types, extracts fields, and categorizes events without requiring manual intervention, thereby reducing time to set up security platforms while maintaining consistent and accurate classification through standardized rules.
Solution Approach 2:
The patent changes the classification process from manual to automated by establishing standardized parameter mappings and categorization rules. This parameter standardization enables automated systems to accurately classify logs based on consistent criteria, eliminating time-consuming manual classification while maintaining high accuracy through structured decision logic.
4Adaptability or versatility
If diverse log formats are supported, then compatibility with multiple tools is improved, but the complexity of reconciling and standardizing formats increases
Solution Approach 1:
The patent creates a universal log format that serves as a common language for multiple cybersecurity tools. The normalized structure with standardized fields (timestamp, host, process, user, message) and categories provides multi-functionality, allowing the system to ingest diverse log formats, reconcile them into a unified structure, and output standardized data that is compatible with various security operations tools without requiring separate standardization logic for each format.
Data Source
AI summary
A system and method for security log classification using an artificial intelligence (AI) model. The method includes obtaining a log comprising a sequence of characters, extracting, using a token vocabulary, a sequence of tokens from the sequence of characters, providing the sequence of tokens as input to a trained artificial intelligence (AI) model, obtaining one or more outputs from the trained AI model, and extracting, from the one or more outputs, (i) a label reflecting a type of log, and (ii) a level of confidence that the label applies to the log.