Cloud Data Platform Anomaly Detection via Polygraph Modeling
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Solution Overview
Problem
Current data monitoring and analytics systems in cloud environments face challenges in efficiently detecting anomalies and managing compute assets, as they often rely on manual processes and lack real-time insights, leading to potential security breaches and inefficiencies in resource management.
Innovation Solution
A data platform is configured to perform various operations within a cloud environment, utilizing agents to collect and report data, which is then processed for anomaly detection, security monitoring, and resource management, leveraging data ingestion, processing, and analytics services to create polygraphs that model normal behavior and detect deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for anomaly detection and data monitoring, then device complexity is reduced, but productivity and measurement precision deteriorate due to lack of real-time insights
Solution Approach 1:
The system automatically collects, processes, and analyzes data from compute assets without requiring manual intervention. Agents autonomously monitor compute assets, detect anomalies, and generate insights, enabling the system to serve itself and eliminate manual monitoring processes while maintaining high detection efficiency
Solution Approach 2:
Manual mechanical monitoring processes are replaced with automated digital systems. The patent implements automated data collection, processing, and analysis mechanisms that substitute human operations, thereby improving productivity while managing complexity through structured automation
2Reliability
If real-time monitoring is implemented, then measurement precision and reliability improve, but loss of time and energy consumption increase
Solution Approach 1:
The system monitors only critical parameters and compute assets that require attention, rather than continuously monitoring everything. By focusing on essential metrics and using selective monitoring strategies, the system maintains high reliability for security monitoring while reducing unnecessary energy consumption from constant comprehensive monitoring
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on detected conditions. When anomalies are detected or risk levels change, the system modifies monitoring intensity and parameters accordingly, maintaining reliable security monitoring during critical periods while reducing energy consumption during normal operation
3Measurement precision
If comprehensive data collection is performed, then measurement precision improves for anomaly detection, but device complexity and loss of information increase
Solution Approach 1:
The system extracts and focuses on the most critical data elements needed for anomaly detection, rather than collecting all available data. By identifying and extracting only essential parameters and metrics, the system achieves high measurement precision for detecting anomalies while minimizing data management overhead and information loss from unnecessary data storage
Solution Approach 2:
The data collection system is segmented into modular components that collect specific types of data from different compute asset categories. This segmentation allows the system to maintain precise anomaly detection by organizing data into manageable segments while reducing overall information management complexity through structured data organization
Data Source
AI summary
Monitoring a software development pipeline, including: retrieving, from one or more components in the software development pipeline, information associated with a software application; identifying, based on the information associated with the software application, an anomaly associated with the software application; and performing one or more remedial actions based on the anomaly.