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An ML/DL framework for Traffic Classification TC

domainDocumentation

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:

GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench

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

About

tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.

tcbenchstack.github.io/tcbench/

Topics

computer-networksdeep-learningmachine-learningtraffic-classification

Resources

Readme
MIT license
Activity

Stars

32 stars

Watchers

4 watching

Forks

5 forks
Report repository

Releases

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Used by

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An ML/DL framework for Traffic Classification TC
domainDocumentation

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:
GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench
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
About
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
tcbenchstack.github.io/tcbench/
Topics
computer-networksdeep-learningmachine-learningtraffic-classification
Resources
Readme
MIT license
Activity
Stars
32 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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GitHub - tcbenchstack/tcbench: tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations. · GitHub
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BranchesTags
Repository files navigation
An ML/DL framework for Traffic Classification TC
domainDocumentation

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:
GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench
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
About
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
tcbenchstack.github.io/tcbench/
Topics
computer-networksdeep-learningmachine-learningtraffic-classification
Resources
Readme
MIT license
Activity
Stars
32 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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var __re = new RegExp('^' + ".*" + '
GitHub - tcbenchstack/tcbench: tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations. · GitHub
Skip to content
BranchesTags
Repository files navigation
An ML/DL framework for Traffic Classification TC
domainDocumentation

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:
GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench
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
About
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
tcbenchstack.github.io/tcbench/
Topics
computer-networksdeep-learningmachine-learningtraffic-classification
Resources
Readme
MIT license
Activity
Stars
32 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - tcbenchstack/tcbench: tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations. · GitHub
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BranchesTags
Repository files navigation
An ML/DL framework for Traffic Classification TC
domainDocumentation

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:
GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench
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
About
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
tcbenchstack.github.io/tcbench/
Topics
computer-networksdeep-learningmachine-learningtraffic-classification
Resources
Readme
MIT license
Activity
Stars
32 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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GitHub - tcbenchstack/tcbench: tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations. · GitHub
Skip to content
BranchesTags
Repository files navigation
An ML/DL framework for Traffic Classification TC
domainDocumentation

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:
GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench
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
About
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
tcbenchstack.github.io/tcbench/
Topics
computer-networksdeep-learningmachine-learningtraffic-classification
Resources
Readme
MIT license
Activity
Stars
32 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - tcbenchstack/tcbench: tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations. · GitHub
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An ML/DL framework for Traffic Classification TC
domainDocumentation

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:
GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench
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
About
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
tcbenchstack.github.io/tcbench/
Topics
computer-networksdeep-learningmachine-learningtraffic-classification
Resources
Readme
MIT license
Activity
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32 stars
Watchers
4 watching
Forks
5 forks
Report repository
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GitHub - tcbenchstack/tcbench: tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations. · GitHub
Skip to content
BranchesTags
Repository files navigation
An ML/DL framework for Traffic Classification TC
domainDocumentation

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:
GoalState of the arttcbench
Data curationThere are a few public datasets for TC, yet no common format/schema, cleaning process, or standard train/val/test folds.An (opinionated) curation of datasets to create easy to use parquet files with associated train/val/test fold.
CodeTC literature has no reference code base for ML/DL modelingtcbench is open source with an easy to use CLI based on click
Model trackingMost of ML framework requires integration with cloud environments and subscription servicestcbench uses aimstack to save on local servers metrics during training which can be later explored via its web UI or aggregated in report summaries using tcbench
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
About
tcbench is a Machine Learning and Deep Learning framework to train model from traffic packet time series or other input representations.
tcbenchstack.github.io/tcbench/
Topics
computer-networksdeep-learningmachine-learningtraffic-classification
Resources
Readme
MIT license
Activity
Stars
32 stars
Watchers
4 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages