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Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis.

Katarzyna Filus¹, Adam Domański², Joanna Domańska¹

¹Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

Neural networks can classify internet traffic data by analyzing self-similarity using the Hurst exponent. This method effectively distinguishes between real and synthetic traffic data, showing promise for network analysis.

Keywords:

Hurst exponent Internet traffic convolutional neural networks fractional Gaussian noise neural networks self-similarity

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Area of Science:

Computer Science
Network Engineering
Data Science

Background:

Internet traffic exhibits complex statistical properties, including self-similarity.
Understanding traffic patterns is crucial for network management and performance optimization.
The Hurst exponent is a key metric for quantifying self-similarity in time series data.

Purpose of the Study:

To evaluate the efficacy of neural networks in classifying internet traffic data based on self-similarity.
To model real internet traffic using synthetic data generated with fractional Gaussian noise.
To investigate the impact of different neural network architectures and training parameters on classification performance.

Main Methods:

Utilized neural networks for internet traffic data classification.
Employed fractional Gaussian noise to generate synthetic data mimicking real traffic characteristics.
Applied the Hurst exponent to quantify self-similarity in both synthetic and real data.
Experimented with various cost function optimizers and numbers of convolutional layers.

Main Results:

The trained neural network model successfully classified synthetic data derived from Pareto distribution.
The model demonstrated capability in classifying real internet traffic data.
Performance varied based on the chosen optimizer and the depth of the convolutional neural network.

Conclusions:

Neural networks are effective tools for classifying internet traffic based on self-similarity.
Fractional Gaussian noise provides a viable method for generating realistic synthetic traffic data.
Network architecture and training optimization are critical factors for achieving high classification accuracy.