Graph Data Ingestion Pipeline for Cybersecurity Visualization
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Solution Overview
Problem
Current cybersecurity analysis systems face challenges in efficiently integrating data from diverse sources into graph databases, leading to delays in visualizing cyber network vulnerabilities and adversarial attacks due to format inconsistencies, the need for custom scripting, and the complexity of updating graph models in real-time.
Innovation Solution
A data pipeline system that converts data from disparate sources into a common node/edge format independently of existing graph models, allowing for near real-time updates of graph models within a cybersecurity analysis system, enabling efficient data ingestion and visualization of cyber network status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from diverse sources is integrated into graph databases using traditional methods, then data integration capability is achieved, but processing time increases and real-time visualization is delayed
Solution Approach 1:
The patent implements a preliminary action by pre-defining a standardized node/edge data format and transformation rules before data ingestion. The system pre-configures mapping templates that automatically translate diverse data sources into the standardized graph format, eliminating the need for ad-hoc format conversion during processing. This preliminary preparation enables rapid data integration without real-time processing delays.
2Adaptability or versatility
If custom scripts are written to handle reformatting of data from different sources, then data format compatibility is achieved, but system complexity increases
Solution Approach 1:
The patent applies universality by creating a universal node/edge data format that can accommodate multiple data sources through a single standardized interface. The transformation engine is designed to handle various input formats (JSON, CSV, XML, database queries) and automatically convert them to the universal graph format using pre-configured mapping rules, eliminating the need for separate custom scripts for each data source.
Solution Approach 2:
The patent introduces an intermediary standardized node/edge format that acts as a mediator between diverse data sources and the graph database. This intermediate representation layer translates heterogeneous data formats into a common structure before ingestion, simplifying the overall system architecture by decoupling data source-specific logic from the core graph processing engine.
3Manufacturing precision
If prior knowledge of graph model structure is required to reformat incoming data, then data integration accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically discover and adapt to incoming data formats without requiring manual configuration of graph model structure knowledge. The transformation engine autonomously maps incoming data fields to the standardized node/edge format using predefined transformation rules and automatic type inference, allowing non-expert users to ingest data from diverse sources without understanding the underlying graph model architecture.
4Loss of information
If large volumes of sensor data are ingested for comprehensive network coverage, then visualization completeness is improved, but processing speed decreases
Solution Approach 1:
The patent applies segmentation by dividing the data ingestion and processing pipeline into independent, parallelizable stages: data source segmentation (handling multiple sources simultaneously), data format segmentation (processing different formats through dedicated transformation rules), and graph update segmentation (incremental updates rather than full reprocessing). This segmented architecture enables comprehensive data ingestion while maintaining processing speed through parallel execution and selective updates.
Data Source
AI summary
Methods and systems are described for assessing a computer network using a graph model. In some instances, the methods comprise: receiving data from at least one data stream of a plurality of data streams, wherein the plurality of data streams comprise computer network data provided by one or more data brokers, and wherein the data received from different data streams of the plurality comprise different data formats; converting the data received from the at least one data stream to a common data format comprising a node or an edge; updating a graph model comprising a plurality of nodes and edges stored within a graph database according to the node or edge of the converted data; and providing a user of the computer network with a visualization of a status of the computer network.