tcbench 0.0.22

An ML/DL framework for Traffic Classification TC

Documentation

tcbench design is cored in the following objectives:

• Easing ML/DL models training/testing results replicability.
• Tight integration with public TC datasets with ease data installation and curation,
• Model tracking via AIM.
• Rich command line for executing modeling campaings and collecting performance reports.

...wait, what is Traffic Classification?

A computer network is formed by hosts that exchange information, namely packets, according to standardized protocols (e.g., HTTP is the protocol used for the web). So to properly operate/manage networks one is required to monitor this flow of information and react accordingly. For instance, in an office/enterprise environment, one might want to prioritize video meeting traffic while limit social media traffic.

Traffic classification is the the act of labeling an exchange of packets between network hosts based on the application that generated it. For instance, you want to identify traffic related to zoom/webx/skype/etc. calls or traffic related to twitter/instagram/facebook/mastodon out of all traffic flowing throught the network.

Motivations

The academic literature is ripe with methods and proposals for TC. Yet, it is scarce of code artifacts and public datasets do not offer common conventions of use.

We designed tcbench with the following goals in mind:

Install

Create a conda environment

conda create -n tcbench python=3.10 pip
conda activate tcbench
python -m pip install tcbench

For the developer version

python -m pip install tcbench[dev]

Features and roadmap

tcbench is still under development, but (as suggested by its name) ultimately aims to be a reference framework for benchmarking multiple ML/DL solutions related to TC.

At the current stage, tcbench offers

• Integration with 4 datasets, namely ucdavis-icdm19, mirage19, mirage22 and utmobilenet21. You can use these datasets and their curated version independently from tcbench. Check out the dataset install process and dataset loading tutorial.

• Good support for flowpic input representation.

• Initial support for for 1d packet time series (based on network packets properties) input representation.

• Data augmentation functionality for flowpic input representation.

• Modeling via XGBoost, vanilla DL supervision and contrastive learning (via SimCLR or SupCon).

More exiting features including more datasets and algorithms will come in the next months.

Stay tuned ;)!

Papers

• "Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation" preprint
  A. Finamore, C. Wang, J. Krolikowski, J. M. Navarro, F. Chen, D. Rossi
  ACM Internet Measurements Conference (IMC), 2023