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nPrintML

Overview

nPrintML bridges the gap between nPrint, which generates standard fingerprints for packets, and AutoML, which allows for optimized model training and traffic analysis. nPrintML enables users with network traffic and labels to perform optimized packet-level traffic analysis without writing any code.

For a high-level overview, installation instructions, and detailed usage information, please visit the project's homepage.

For brief installation and usage instructions, or for the Docker container demo, see below.

Set-up

Dependencies

Python versions 3.6 through 3.8 are supported.

You might check what versions of Python are installed on your system, e.g.:

ls -1 /usr/bin/python*

As needed, consult your package manager or python.org.

Depending on your situation, consider pyenv for easy installation and management of arbitrary versions of Python.

nPrintML further requires nPrint (see below).

Installation

nPrintML itself is available for download from the Python Package Index (PyPI) and via pip, e.g.:

python -m pip install nprintml

(Note: The python or pip executable used to install nPrintML depends upon your system and as discussed in the preceding section. And, for example, your system might supply a python3 command, but not a python command. As such, the above may be adapted to your system.)

This downloads, builds and installs the nprintml console command. If you're happy to manage your Python (virtual) environment, you're all set with the above.

That said, installation of this command via a tool such as pipx is strongly encouraged. pipx will ensure that nPrintML is installed into its own virtual environment, such that its third-party libraries do not conflict with any others installed on your system.

(Note that nPrint and nPrintML are unrelated to the PyPI distribution named "nprint.")

Post-installation

nPrintML depends on the nPrint command, which may be installed separately, (with reference to the nPrint documentation).

For quick-and-easy satisfaction of this requirement, nPrintML supplies the bootstrapping command nprint-install, which is made available to your environment with nPrintML installed. This command will inspect its execution environment and attempt to retrieve, compile and install nPrint with the most appropriate defaults:

nprint-install

nPrint may thereby be installed system-globally, to the user environment, to the (virtual) environment to which nPrintML was installed, or to a specified path prefix. Consult the command's --help for more information.

nprint-install is identically available through its Python module (no different from pip above):

python -m nprintml.net.install

Further set-up

nPrintML leverages AutoGluon to manage AutoML. However, it does not by default install additional libraries required for all models supported by AutoGluon. If you wish to test these models, you will need to install their requirements manually.

AutoGluon will itself note which models it is unable to generate – and how to satisfy their requirements – during operation.

For more information, consult the AutoGluon documentation.

Usage

nPrintML supplies the top-level shell command nprintml

nprintml ...

– as well as its terse alias nml

nml ...

In case of command path ambiguity and in support of debugging, the nprintml command is also available through its Python module:

python -m nprintml ...

For detailed usage information with full examples of how to run nPrintML, please visit our homepage.

Demo

A Docker container demo is provided of each nPrintML release to aid prospective users in trying out the tool.

Note: The container is intended for users of as-yet-unsupported platforms and users of atypically-configured systems. nPrintML should install easily for most users, as described in the preceding section; and, for day-to-day use, it is strongly recommended that nPrintML be installed without virtualization. nPrintML users requiring support in installing the tool should consult the project's homepage and then consider reaching out for help. Users of unsupported platforms should consider creating a feature request or a pull request.

Dependencies

The container demo requires Docker.

Usage

The container entrypoint is the nprintml command, and as such takes the same arguments:

docker run [...] ghcr.io/nprint/nprintml ...

Note, however, that any argument references to the host filesystem must be mapped to the container filesystem. For this reason, the nprintml-docker script is recommended.

Demo script

The nprintml-docker script is the recommended interface to the container demo. Argument references to the host filesystem are mapped and rewritten for the container filesystem; outputs are written to the host filesystem and given user ownership, etc.

nprintml-docker requires Python v3.6 or greater.

The script is available for download from the nPrintML repository. It may be placed anywhere on your system, and made executable or invoked with either python or python3.

A reasonable installation of the script (globally) might include the following:

DEST="/usr/local/bin"

curl --output-dir "$DEST" -O https://raw.githubusercontent.com/nprint/nprintML/main/image/nprintml-docker

chmod +x "$DEST"/nprintml-docker

The script should then be available for execution, even without reference to its full path, (so long as DEST above is itself in your environment's PATH):

nprintml-docker ...

Note: In the above example, the script is installed to the system globally. This will likely require root permission – e.g. sudo curl … and sudo chmod ….

Alternatively, set a different DEST. The script may be placed wherever you have write access – including user directories which are also commonly placed on PATH: $HOME/.local/bin, $HOME/bin, etc.

If DEST is not on PATH, the script may be invoked via its full download path, e.g.:

~/Downloads/nprintml-docker ...

And if the script is not made executable, it may be invoked via any available python (v3.6+):

python ~/Downloads/nprintml-docker ...
Environment variables

The script's interface is forwarded to nprintml within the demo container. The script itself may be configured via the following environment variables.

Name Values Description
NPRINTML_DOCKER_CHOWN 0, 1* Outputs owned by root are given user ownership via chown (1)
NPRINTML_DOCKER_DEBUG 0*, 1 Enable debug-level logging to standard output (1)
NPRINTML_DOCKER_REMOVE 0, 1* Container is removed after each run (1)
NPRINTML_DOCKER_REPOSITORY (repository: ghcr.io/nprint/nprintml*) nPrintML repository of image
NPRINTML_DOCKER_VERSION (version: latest*) nPrintML image version tag

* default value

Development

Development requirements may be installed via the dev extra (below assuming a source checkout):

pip install --editable .[dev]

(Note: The installation flag --editable is also used above to instruct pip to place the source checkout directory itself onto the Python path, to ensure that any changes to the source are reflected in Python imports.)

Development tasks are then managed via argcmdr sub-commands of manage …, (as defined by the repository module manage.py), e.g.:

manage version patch -m "initial release of nprintml" \
       --build                                        \
       --release

Citing nPrintML

@inproceedings{10.1145/3460120.3484758,
author = {Holland, Jordan and Schmitt, Paul and Feamster, Nick and Mittal, Prateek},
title = {New Directions in Automated Traffic Analysis},
year = {2021},
isbn = {9781450384544},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3460120.3484758},
doi = {10.1145/3460120.3484758},
pages = {3366–3383},
numpages = {18},
keywords = {machine learning on network traffic, automated traffic analysis, network traffic analysis},
location = {Virtual Event, Republic of Korea},
series = {CCS '21}
}

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nPrintML creates and runs full ML-based traffic analysis pipelines using nPrint and AutoML.

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