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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression

This repository contains all the digital artifacts associated with the paper "AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification."

Repository Structure

1. VanillaAE

This Jupyter Notebook contains all resources related to the Vanilla Autoencoder used in the paper. It includes the entire workflow:

  • Data Preprocessing: Includes steps like outlier removal and robust scaling.
  • Autoencoder Architecture: Details of the Vanilla Autoencoder model used for IP flow record compression.
  • Training Process: Training configuration, loss function, optimizer, and training duration.
  • Results Analysis: Contains visualizations and metrics for model performance.

2. VanillaAEvsDAE

This Jupyter Notebook provides a comparative analysis between the Vanilla Autoencoder and the Denoising Autoencoder (DAE):

How to Use

  1. Run the Jupyter Notebook: Open VanillaAE.ipynb to reproduce the experiments and results related to the Vanilla Autoencoder.
  2. Model Comparisons: Check VanillaAEvsDAE.ipynb for the comparison between Vanilla AE and Denoising AE.