Vent is a light-weight platform built to automate network collection and analysis pipelines using a flexible set of popular open source tools and technologies. Vent is python-based, extensible, leverages docker containers, and provides both an API and CLI.
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Latest commit 0f6181a Nov 1, 2017 @cglewis cglewis Merge pull request #963 from Tranquilled/label-maintainer
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README.md

vent

Network Visibility (an anagram)

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overview

vent is a library that includes a CLI designed to serve as a general platform for analyzing network traffic. Built with some basic functionality, vent serves as a user-friendly platform to build custom plugins that perform user-defined processing on incoming network data. vent is filetype-agnostic in that the plugins installed within your specific vent instance determine what type of files your instance supports.

Simply create your plugins, point vent to them & install them, and drop a file in vent to begin processing!

dependencies

docker>=1.13.1
git
make (if building from source)
pip
python2.7.x

option 1: running inside of a Docker container

docker run -it -v /var/run/docker.sock:/var/run/docker.sock cyberreboot/vent

option 2: installing

pip install vent

option 3: getting the bits and building

git clone https://github.com/CyberReboot/vent.git
cd vent

Root/sudo users can simply run make to compile and install the platform. Users with limited permissions or require user-local installation can use the following:

sudo env "PATH=$PATH" make

Note - If you already have docker-py installed on your machine, you may need to pip uninstall docker-py first. vent will install docker-py as part of the installation process, however there are known incompatibilities of docker-py with older versions.

option 4: deploying with an ISO

go to releases and download the ISO from the latest release (or build your own: cd dev && make) deploy the ISO as a VM or on bare metal.

running

vent

documentation

Want to read the documentation for vent? Great! You can find it here

contributing to vent

Want to contribute? Awesome! Issue a pull request or see more details here.

See this for a crash course on npyscreen: the TUI used by Vent!