Metadata-Driven Feature Engineering for Log Data Processing
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Solution Overview
Problem
Traditional malware detection methods require developing separate vector creation programs for each type of log file, leading to inefficiency and potential bias due to inconsistent data input, as new features and log formats emerge, necessitating frequent updates and maintenance.
Innovation Solution
A metadata-driven feature engineering system that uses a single program to accommodate various log file types, formats, and nomenclatures, with a centralized dictionary and regular expression management system to generate unit operations for processing and formatting log data into vectors compatible with machine learning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate vector creation programs are developed for each log file type, then each program can be optimized for its specific format, but the system complexity increases and maintenance becomes inefficient
Solution Approach 1:
The patent implements a universal vector creation program that can process multiple log file types through a metadata-driven architecture. The system uses a centralized dictionary that maps different log formats to standardized feature representations, allowing a single program to handle various log types consistently without requiring separate specialized programs for each format.
Solution Approach 2:
The patent introduces a metadata layer and centralized dictionary as an intermediary between diverse log file formats and the vector creation process. This metadata-driven approach acts as a mediator that translates different log structures into a unified representation, enabling consistent processing without direct customization for each log type.
2Adaptability or versatility
If separate vector creation programs are maintained for different log formats, then each can handle its specific format, but maintenance efforts and potential errors increase
Solution Approach 1:
The system provides a single maintainable program that supports multiple log formats through configuration-based metadata rather than code-based format-specific logic. Updates to handling new log formats or fixing issues require only metadata dictionary modifications, not program restructuring, significantly improving ease of repair while maintaining versatility.
Solution Approach 2:
The patent uses parameter changes in the metadata dictionary to adapt the system to different log formats and new features. By changing parameters in the centralized dictionary (such as adding new feature mappings or format specifications), the system can accommodate new log types and fix issues without modifying the core program logic, making maintenance efficient and error-resistant.
3Reliability
If traditional feature engineering methods are used with multiple separate programs, then each program can be specialized, but productivity and efficiency decrease
Solution Approach 1:
The patent merges multiple separate vector creation programs into a single unified program that processes all log types. By combining the functionality of multiple specialized programs into one metadata-driven system, the patent eliminates the overhead of managing multiple programs while maintaining consistent feature engineering across all log formats, thereby improving productivity without sacrificing reliability.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data.