Polygraph-Based Cloud Data Anomaly Detection
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Solution Overview
Problem
Current data monitoring and anomaly detection systems in cloud environments face challenges in efficiently collecting and processing vast amounts of data from diverse sources, leading to difficulties in real-time anomaly detection and security monitoring within datacenters.
Innovation Solution
A data platform is configured to ingest data from cloud environments using agents deployed on compute assets, which collect and report information on network activities, processes, and user interactions, and then utilize polygraphs to model behaviors and detect anomalies by constructing logical graphs of entities connected by their behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is collected from diverse cloud environment sources, then monitoring coverage is improved, but data complexity increases
Solution Approach 1:
The patent introduces polygraphs as an intermediary data structure that mediates between raw data from diverse cloud sources and the monitoring analysis layer. Polygraphs transform complex, heterogeneous data into a standardized logical graph representation with nodes for entities and edges for relationships, enabling unified monitoring across multiple data sources without increasing operational complexity
Solution Approach 2:
The system segments data processing into distinct stages: data collection from various sources, polygraph construction as an intermediate representation, and anomaly detection. This segmentation allows each component to handle specific data types independently while maintaining overall system simplicity through modular architecture
2Reliability
If real-time anomaly detection is implemented, then security monitoring is improved, but processing speed requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously constructing and maintaining polygraph representations of normal behavior patterns. Baseline polygraphs are built in advance from learned normal behavior, enabling rapid anomaly detection when actual behavior deviates from these pre-established models without requiring complex real-time analysis
Solution Approach 2:
The patent changes the parameter representation from raw heterogeneous data to standardized polygraph structures with consistent node and edge types. This parameter transformation enables efficient comparison against baseline behavior using simple graph traversal and matching algorithms, significantly improving processing speed while maintaining detection accuracy
3Productivity
If data processing efficiency is improved, then monitoring performance is improved, but data volume handling becomes more difficult
Solution Approach 1:
The system extracts only the essential behavioral relationships from vast amounts of raw cloud data and represents them as polygraphs. By taking out only the critical connectivity and behavior patterns rather than processing all raw data, the system achieves high processing efficiency while maintaining the ability to handle large data volumes through compact graph representations
Data Source
AI summary
Dynamically generating monitoring tools for software applications, including: inspecting, using static code analysis, a non-executable representation of the application to identify one or more points in an application for monitoring; and for each of the one or more points in the application: generating a monitoring program; and inserting, into an executable representation of the application, the monitoring program at a location in the executable representation of the application that corresponds to the identified point in the application.