Network Attribute Change Detection Using Embedding Vectors
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Solution Overview
Problem
Existing network monitoring systems struggle to efficiently detect changes in network attributes due to the need for extensive and complex database queries, which can be computationally expensive and computationally inefficient, especially when dealing with large numbers of dimension combinations, as they require N×M queries to identify which attributes have changed, making them computationally inefficient and time-consuming.
Innovation Solution
A system and method that utilizes a machine learning model to generate embedding vectors to represent network attributes, allowing for nearest neighbor processing within a vector space to identify changes in network attributes, reducing the need for extensive querying and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional database querying methods are used to detect network attribute changes, then measurement precision can be maintained, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent replaces traditional mechanical database querying operations with a machine learning-based embedding vector system. Instead of performing N×M database queries to compare network attributes, the system converts attributes into embedding vectors and uses nearest neighbor search in vector space, substituting complex mechanical querying with efficient vector-based computational geometry operations.
Solution Approach 2:
The patent transforms network attributes from discrete database records into continuous embedding vector representations. This parameter change allows the system to work with dense vector embeddings rather than sparse database entries, enabling the use of vector space operations and nearest neighbor algorithms that are computationally more efficient than traditional database querying methods.
2Reliability
If traditional database querying methods are used to detect network attribute changes, then detection capability is maintained, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-computing embedding vectors for network attributes and storing them in a vector database before actual change detection is needed. When network attributes change, the system only needs to perform a single nearest neighbor search in the pre-built vector space rather than querying the entire database, significantly reducing detection time while maintaining reliability.
Solution Approach 2:
The patent substitutes time-consuming database querying operations with efficient vector space nearest neighbor search. The embedding vector representation allows the system to leverage optimized vector search algorithms that are significantly faster than traditional database queries, reducing time consumption while preserving detection capability.
3Productivity
If embedding vectors are used to represent network attributes, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent introduces embedding vectors as an intermediary layer between raw network attributes and the change detection system. This intermediary transformation enables the use of efficient vector space operations and nearest neighbor search, improving processing efficiency. The added complexity is localized to the embedding generation step, while the core detection mechanism becomes simpler and more efficient.
Data Source
AI summary
Systems and methods for network traffic monitoring are provided. A system may retrieve first information of a first data packet exchange including a first plurality of network attributes associated with the first data packet exchange, generate a first embedding vector corresponding to the first plurality of network attributes, identify a second embedding vector of a second data packet exchange based on a correlation between the first embedding vector and the second embedding vector in a vector space, determine that one or more network attributes are included in the first information and absent from second information of the second data packet exchange, and generate an entry in the second database to include the first information and a flag to indicate the determination that the one or more network attributes are included in the first information and absent from the second information.