Threat Intelligence System Data Normalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cybersecurity systems face challenges in effectively aggregating and processing scattered, incompatible, and inaccurate threat data from various sources, leading to inefficiencies in threat detection and response.
Innovation Solution
A computer-implemented method that imports threat data from multiple sources, processes it to remove redundancies and false positives, converts it into a common format, and generates a master threat data definition compatible with client devices, utilizing sources like social networks, honeypot servers, and open-source feeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If threat data is imported from multiple diverse sources, then the quantity and variety of threat information increases, but the data becomes scattered, incompatible, and contains inaccuracies
Solution Approach 1:
The patent introduces a centralized threat intelligence platform as an intermediary system that receives data from multiple diverse sources, processes and validates it through standardized procedures, and outputs cleaned, formatted threat intelligence. This intermediary layer resolves the contradiction by acting as a buffer between raw diverse data and final reliable output, implementing data validation rules and normalization processes that filter inaccuracies while maintaining comprehensive coverage.
Solution Approach 2:
The system transforms threat data by changing its parameters - converting various formats into a unified standardized format, adjusting data structures to common schemas, and transforming raw data into processed intelligence. This parameter transformation resolves the incompatibility issue while maintaining the quantity of information, as the data is rewritten in a consistent representation that can be reliably processed and shared.
2Adaptability or versatility
If threat data from multiple sources is aggregated, then comprehensive threat intelligence is achieved, but the system complexity increases
Solution Approach 1:
The patent divides the complex data aggregation and processing system into distinct modular components: data collection modules for different source types, data validation modules, data normalization modules, and threat intelligence generation modules. This segmentation allows the system to handle comprehensive data from multiple sources while maintaining manageable complexity through independent, reusable components that can be configured and scaled separately.
Solution Approach 2:
The system implements a universal processing framework that can handle multiple data sources and formats through a single standardized pipeline. The same core processing logic can accommodate different input formats by applying appropriate parsers and transformers, reducing overall system complexity compared to separate specialized systems for each data source while maintaining comprehensive detection capability.
3Measurement precision
If raw threat data is processed and cleaned to remove false positives, then data accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data validation and filtering actions during the data collection and processing stages, identifying and removing false positives before the final threat intelligence generation. By conducting data quality checks early in the pipeline rather than as a final step, the system reduces overall processing time while maintaining high precision, as incorrect data is eliminated before requiring extensive analysis.
Solution Approach 2:
The patent implements continuous processing where data validation, cleaning, and verification operations occur concurrently with data collection and processing rather than as separate sequential steps. Multiple processing operations execute in parallel on different data streams, maintaining continuous useful action that improves throughput. This continuous approach processes large volumes of data efficiently while maintaining high precision through ongoing validation.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for importing threat data from a plurality of threat data sources, thus generating a plurality of raw threat data definitions. The plurality of raw threat data definitions are processed, thus generating a plurality of processed threat data definitions. The plurality of processed threat data definitions are processed to form a master threat data definition. The master threat data definition is provided to one or more client electronic devices.