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Utilizing TabNet Deep Learning for Elephant Flow Detection by Analyzing Information in First Packet Headers.

Bartosz Kądziołka¹, Piotr Jurkiewicz¹, Robert Wójcik¹

¹Institute of Telecommunications, AGH University of Krakow, 30-054 Krakow, Poland.

Entropy (Basel, Switzerland)

|July 26, 2024

Summary

This summary is machine-generated.

This study uses TabNet deep learning to detect large network data flows, called elephant flows, from initial packet headers. This method significantly reduces flow table entries, improving network traffic management efficiency.

Keywords:

TabNet elephant feature importance flow table flows input information machine learning mice traffic engineering

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Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Efficient network traffic management requires rapid detection of significant data streams.
Identifying large-scale data flows (elephant flows) is critical for optimizing network performance.
Traditional methods may struggle with the scale and speed of modern network traffic.

Purpose of the Study:

To investigate the effectiveness of the TabNet deep learning architecture for identifying elephant flows.
To analyze the 5-tuple fields of initial packet headers for flow characteristics.
To assess the potential for reducing flow table entries in network devices.

Main Methods:

Utilized the TabNet deep learning model for flow analysis.
Analyzed 5-tuple information from the initial packet header.
Trained and tested the model on a campus network dataset.

Main Results:

The TabNet model accurately identifies elephant flows at the beginning of their transmission.
The number of required flow table entries was reduced by up to 20 times.
Effective management of 80% of network traffic was achieved using individual flow entries.

Conclusions:

TabNet offers a robust and accurate solution for real-time elephant flow detection.
This approach significantly enhances network traffic management efficiency and scalability.
The model's performance on a campus network dataset suggests broad applicability across diverse network environments.