Cloud Data Ingestion via Polygraph Templates for Anomaly Detection
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Solution Overview
Problem
Current data monitoring and analytics systems in cloud environments face challenges in efficiently detecting anomalies and managing data across multiple agents and compute assets, leading to potential security breaches and operational inefficiencies.
Innovation Solution
A data platform is configured to ingest data from cloud environments, process it using agents deployed on compute assets, and generate polygraphs to model behaviors, enabling real-time anomaly detection and dynamic monitoring tools for software applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected from multiple agents and compute assets in cloud environments, then monitoring coverage and security detection capability are improved, but system complexity and data management difficulty increase
Solution Approach 1:
The system segments data collection by deploying agents on individual compute assets, organizing data into streams grouped by compute asset, and processing them through separate pipelines. This segmentation allows the system to handle multiple data sources without overwhelming complexity, as each segment can be processed independently through standardized procedures.
Solution Approach 2:
The system introduces an intermediary processing layer between data collection and analysis. Compute asset streams serve as intermediaries that aggregate and pre-process data from multiple agents before the data reaches the final analysis stage. This intermediary layer simplifies the overall system architecture by absorbing complexity at intermediate stages.
2Speed
If real-time data processing is implemented to detect anomalies, then response time to security breaches is improved, but computational resources and processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-defining polygraph templates and baseline behaviors before anomalies occur. Agents continuously compare incoming data against these pre-established templates, enabling real-time detection without requiring complex computational analysis at the moment of anomaly detection. The heavy computational work is done in advance during template creation.
Solution Approach 2:
The system creates simplified representations (copies) of complex data patterns through polygraph templates. Instead of processing raw complex data streams in real-time, the system compares simplified template representations against incoming data, significantly reducing computational requirements while maintaining detection capability.
3Measurement precision
If polygraphs are generated to model behaviors, then anomaly detection accuracy is improved, but data processing time and computational overhead increase
Solution Approach 1:
The system changes parameters by transforming detailed event data into summarized polygraph representations with key behavioral parameters. This parameter transformation occurs during the data collection phase, where agents aggregate and summarize data before it reaches the analysis stage, reducing processing time while maintaining the precision needed for accurate anomaly detection.
Data Source
AI summary
Ingesting event data into a data warehouse, including: receiving first event data comprising a plurality of first records each describing an event of a plurality of events occurring within a computing environment, wherein the event data is encoded in a first storage format; generating, based on the first event data, second event data comprising plurality of second records encoded in a second storage format for storage in a data warehouse; and sending a plurality of files comprising the second event data to the data warehouse.