Network Equipment Behavioral Profiling From Inferred Operating Parameters
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Solution Overview
Problem
Optimizing and securing communication networks is challenging without accurate knowledge of internal parameters and characteristics of network equipment and devices, leading to costly and inefficient troubleshooting.
Innovation Solution
A method and apparatus for inferentially obtaining and predicting operational parameters of network equipment using passive and active data, employing machine learning and artificial intelligence to manage and troubleshoot networks effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network management methods are used without internal parameters knowledge, then network operation can be maintained, but optimization and troubleshooting become challenging and costly
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between network operators and network equipment internal parameters. This system infers and predicts internal parameters (such as scheduler algorithms, buffer states, and device characteristics) by analyzing observable network traffic and performance data, thereby providing the necessary information for effective optimization and troubleshooting without direct access to equipment internals
Solution Approach 2:
The patent replaces the mechanical approach of directly accessing physical equipment parameters with an information-based system that uses machine learning and data analysis to infer parameters. Instead of physically inspecting or directly reading internal device states, the system substitutes this with computational modeling and prediction algorithms that derive equivalent information from observable network behavior
2Measurement precision
If internal parameters of network equipment are directly accessed, then accurate troubleshooting is achieved, but device complexity and access difficulty increase
Solution Approach 1:
The patent creates a virtual copy or model of the network equipment's internal state by inferring parameters from external observations. Instead of directly accessing the actual internal parameters of network devices, the system builds a replicated representation of these parameters through machine learning models trained on network traffic data, enabling accurate analysis without physical access to device internals
Solution Approach 2:
The system introduces an intermediary layer that translates difficult-to-access internal parameters into observable network performance metrics. This intermediary inference system bridges the gap between internal device states and external measurements, making hidden parameters accessible through intelligent analysis of network traffic patterns and performance data
3Measurement precision
If comprehensive network data is collected for analysis, then accurate predictions are achieved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant and informative features from the comprehensive network data collected. Instead of processing all available data, the system identifies and extracts key features that are most predictive of internal parameters and device behavior, reducing computational complexity while maintaining prediction accuracy through selective feature extraction and dimensionality reduction techniques
Data Source
AI summary
Aspects of the subject disclosure may include, for example, receiving data associated with network equipment operating in a communication system, determining from the data inferential parameters that describe operations of the network equipment; and determining, from the inferential parameters, one or more behavioral profiles of one or more network elements of the network equipment. Other embodiments are disclosed.