IP Address Vector Embeddings for Network Behavior Analysis
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Solution Overview
Problem
Current machine learning representations of IP addresses fail to capture network behavior information, as they are based on network topology and only provide distance metrics, not reflecting actual traffic pattern behaviors of hosts.
Innovation Solution
The method involves learning vector embeddings of IP addresses and connection contexts jointly in a machine learning model, using a processor to initialize embeddings with IP addresses and incorporating context-specific weights, such as through term frequency-inverse document frequency (TF-IDF), to enhance representation of network behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If machine learning models use traditional IP address representations based on network topology, then the model structure is simple, but the representation fails to capture actual network behavior information
Solution Approach 1:
The patent transforms IP addresses from traditional numerical or topological representations into vector embeddings in a continuous vector space. This parameter transformation allows the model to capture network behavior patterns while maintaining computational efficiency. The vector embeddings are trained to preserve both topological relationships and behavioral characteristics of hosts.
Solution Approach 2:
The patent introduces a new dimensional representation by mapping discrete IP addresses into continuous vector embeddings with multiple dimensions. This dimensional transformation enables the model to encode complex network behavior information that cannot be captured in traditional one-dimensional or topological representations, while still allowing for efficient distance computation.
2Measurement precision
If machine learning models use detailed connection attributes as features, then network behavior representation accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent merges multiple connection attributes (source IP, destination IP, source port, destination port, protocol, bytes, packets, timestamps) into unified vector embeddings for both IP addresses and connection contexts. This consolidation reduces the complexity of processing individual attributes while preserving the comprehensive network behavior information through the learned embeddings.
Solution Approach 2:
The patent introduces connection context embeddings as an intermediary representation that captures the semantic meaning of connection attributes. These embeddings serve as a bridge between raw connection data and the final network behavior analysis, simplifying the processing pipeline while maintaining high representation accuracy.
Data Source
AI summary
According to examples, an apparatus may include a processor and a non-transitory computer readable medium on which is stored machine readable instructions that may cause the processor to identify Internet protocol (IP) addresses and connection attributes associated with the IP addresses. The instructions may also cause the processor to train a machine learning model using the IP addresses as inputs to the machine learning model and connection contexts as outputs of the machine learning model. The machine learning model may learn a first weight matrix corresponding to the IP addresses and a second weight matrix corresponding to the connection contexts. In addition, the connection contexts may be concatenations of the connection attributes associated with a corresponding IP address.