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Measuring and classifying IP usage scenarios: a continuous neural trees approach.

Zhenhui Li¹, Fan Zhou^1,2, Zhiyuan Wang¹

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|March 1, 2024

Summary

This study introduces a new method to classify IP address usage scenarios, distinguishing between enterprise and home networks. The model effectively identifies usage patterns and generalizes across different regions.

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Area of Science:

Computer Science
Network Security
Machine Learning

Background:

Understanding user behavior through IP addresses is vital for applications like fraud prevention and marketing.
Existing methods focus on IP geolocation and anomaly detection, neglecting IP usage scenario classification.
The function of an IP address (e.g., private enterprise vs. home broadband) is an underexplored area.

Purpose of the Study:

To initiate the first attempt at classifying IP usage scenarios.
To develop a model capable of learning IP assignment rules and complex feature interactions.
To evaluate the model's classification accuracy and generalizability across different regions.

Main Methods:

Collected IP address data from four large-scale regions.
Proposed a novel continuous neural tree-based ensemble model.
Conducted extensive experiments to evaluate performance.

Main Results:

The proposed model efficiently uncovers significant higher-order feature interactions.
Enhanced classification accuracy for IP usage scenarios.
Demonstrated the model's ability to generalize from source to target regions.

Conclusions:

The developed model effectively classifies IP usage scenarios.
The approach enhances understanding of IP address functions.
The model shows strong generalizability, applicable across diverse network environments.