Network Service Dependency Detection via Concept Lattices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network service dependency detection tools fail to utilize all available data and often miss interactions between more than two services, leading to 'blind spots' and limited understanding of network service relationships, especially in passive dependency inference methods.
Innovation Solution
A system utilizing formal concept analysis to generate context tables and lattices from packet headers and attributes, identifying network services and their dependencies by classifying nodes and monitoring temporal relationships between services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If passive dependency inference methods are used, then network monitoring is transparent and no additional traffic is required, but blind spots occur where traffic does not pass through packet collection points
Solution Approach 1:
The patent combines multiple data sources including packet capture data, flow data, and service dependency information into a unified analysis framework. This merging allows the system to cross-validate findings and reduce blind spots while maintaining passive monitoring capabilities.
Solution Approach 2:
The patent introduces an intermediary layer of flow data and metadata that bridges the gap between packet capture points and service dependency detection. This intermediary data helps infer dependencies even when direct packet observation is unavailable.
2Measurement precision
If traditional dependency detection tools are used, then some service dependencies can be identified, but interactions between more than two services are not fully considered
Solution Approach 1:
The patent transitions from pairwise service analysis to multi-dimensional service chain analysis by incorporating temporal sequences and multiple service interactions. This allows detection of complex dependencies involving more than two services while managing complexity through structured lattice operations.
Solution Approach 2:
The patent employs nested concept lattices where service dependencies are organized in hierarchical structures. Inner lattices represent individual service relationships while outer lattices capture broader service chain dependencies, enabling systematic analysis of complex multi-service interactions.
3Measurement precision
If all available data is utilized for dependency detection, then detection accuracy improves, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the most relevant features and attributes from available data sources that are necessary for dependency detection. By selectively extracting key characteristics rather than processing all raw data, the system maintains high detection accuracy while reducing processing complexity.
Solution Approach 2:
The patent segments the data processing task into distinct stages: data collection, feature extraction, lattice construction, and dependency inference. This segmentation allows each stage to process only necessary data with appropriate complexity, improving overall efficiency.
Data Source
AI summary
Described is system for automatically detecting network services and their dependencies. The system generates a first context table having rows of packet headers and columns of header field values. A first concept lattice is generated from the first context table, and network services and corresponding packet headers are identified. A second context table is generated using the networks services data, and a second concept lattice is generated from the second context table. Network service dependencies are identified using the second concept lattice. The context tables are used to monitor the plurality of network service dependencies.