Application Traffic Flow Analytics for Network Sub-Component Threat Detection
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Solution Overview
Problem
Existing network monitoring systems struggle to accurately detect and mitigate issues on a sub-component level within network devices, as they are typically monitored on a per-device basis, making it difficult to isolate and address bottlenecks effectively.
Innovation Solution
The technology disaggregates network devices into sub-components and monitors functional flows across these components, generating analytics on a sub-component basis to provide a unified view of internal and external communications, enabling more precise issue detection and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network devices are monitored on a per-device basis, then the monitoring system is simple to implement, but it is difficult to accurately detect and isolate problems at the sub-component level
Solution Approach 1:
The patent applies segmentation by breaking down network devices into discrete sub-components (such as interface cards, processors, and memory modules) and monitoring each sub-component independently. This allows precise identification of which specific sub-component is causing network issues, transforming the monitoring granularities from device-level to sub-component-level without overwhelming complexity through modular monitoring architecture.
Solution Approach 2:
The patent introduces an intermediary monitoring system that sits between the network devices and the analysis layer. This intermediary collects data from multiple sub-components, normalizes it, and presents it in a unified manner, thereby shielding the complexity of sub-component monitoring from end users while maintaining high detection precision.
2Measurement precision
If network devices are disaggregated into sub-components for monitoring, then problem isolation precision is improved, but the monitoring and analysis complexity increases
Solution Approach 1:
The patent segments functional flows into discrete traceable units that can be followed across sub-component boundaries. Each functional flow is broken down into individual operations at each sub-component, allowing precise bottleneck isolation while maintaining manageable complexity through structured flow representation.
Solution Approach 2:
The patent implements feedback mechanisms where monitoring data from sub-components is continuously analyzed and fed back to adjust monitoring parameters and focus resources on problematic areas. This feedback loop enables precise bottleneck identification while optimizing monitoring complexity by concentrating analysis on relevant sub-components and functional flows.
3Measurement precision
If extensive testing is performed to detect sub-component problems, then detection accuracy is improved, but the time and resources required increase significantly
Solution Approach 1:
The patent applies preliminary action by implementing continuous passive monitoring of sub-components that captures baseline performance data and anomaly patterns before problems occur. This preliminary data collection enables rapid problem detection without requiring extensive reactive testing, as the system is already gathering relevant information in real-time.
Solution Approach 2:
The patent replaces manual or active testing mechanisms with automated electronic monitoring and analysis systems. Sensors and software agents continuously collect and analyze sub-component data, substituting time-consuming manual testing with automated real-time detection that achieves high accuracy without significant time investment.
Data Source
AI summary
The present technology pertains to identifying one or more sub-components of one or more network devices in a network environment; disaggregating the one or more sub-components from corresponding network devices of the one or more network devices; monitoring one or more functional flows across the one or more sub-components on a sub-component basis to generate functional flow data associated with the one or more sub-components; and generating analytics of the network environment on a sub-component basis from the functional flow data.