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66
README.rst
66
README.rst
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@ -1,21 +1,56 @@
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========================
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|nfstream_logo| nfstream
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========================
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.. image:: https://github.com/aouinizied/nfstream/blob/master/docs/nfstream_logo.png
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:scale: 100%
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:align: left
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|build| |doc| |download| |release| |python| |pypy| |platform| |license|
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########
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nfstream is a flexible and lightweight network data analysis framework.
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NFStream
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########
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**nfstream main features**
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* **Performance:** nfstream was designed to be fast with a small CPU and memory footprint.
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* **Layer-7 visibility:** nfstream dissection is based on nDPI_ (~300 applications including Tor, Messenger, WhatsApp, etc.).
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|release| |python| |pypy| |platform| |license|
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NFStream is a flexible and lightweight network data analysis framework.
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.. list-table::
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:widths: 25 25
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:header-rows: 0
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* - Build Status
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- |build|
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* - Documentation Status
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- |doc|
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* - Coverage
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- |coverage|
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* - Code Quality
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- |quality|
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* - Downloads
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- |download|
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**NFStream main features**
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* **Performance:** NFStream was designed to be fast with a small CPU and memory footprint.
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* **Layer-7 visibility:** NFStream dissection is based on nDPI_ (~300 applications including Tor, Messenger, WhatsApp, etc.).
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* **Flexibility:** add a flow feature in 2 lines as an NFPlugin_.
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* **Machine Learning oriented:** add your trained model as an NFPlugin_.
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NFStream is a flexible and lightweight network data analysis framework.
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**NFStream main features**
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* **Performance:** NFStream was designed to be fast with a small CPU and memory footprint.
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* **Layer-7 visibility:** NFStream dissection is based on nDPI_ (~300 applications including Tor, Messenger, WhatsApp, etc.).
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* **Flexibility:** add a flow feature in 2 lines as an NFPlugin_.
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* **Machine Learning oriented:** add your trained model as an NFPlugin_.
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**Use**
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* Dealing with a big pcap file and just want to aggregate it as network flows? nfstream make this path easier in few lines:
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* Dealing with a big pcap file and just want to aggregate it as network flows? NFStream make this path easier in few lines:
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.. code-block:: python
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@ -101,7 +136,7 @@ Binary installers for the latest released version are available:
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from source
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^^^^^^^^^^^
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If you want to build nfstream on your local machine:
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If you want to build NFStream on your local machine:
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.. code-block:: bash
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@ -121,16 +156,16 @@ requests to us.
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Authors
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=======
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`Zied Aouini`_ (`aouinizied`_) created nfstream and `these fine people`_
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`Zied Aouini`_ (`aouinizied`_) created NFStream and `these fine people`_
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have contributed.
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Ethics
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=======
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nfstream is intended for network data research and forensics.
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NFStream is intended for network data research and forensics.
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Researchers and network data scientists can use these framework to build reliable datasets, train and evaluate
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network applied machine learning models.
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As with any packet monitoring tool, nfstream could potentially be misused.
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As with any packet monitoring tool, NFStream could potentially be misused.
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**Do not run it on any network of which you are not the owner or the administrator**.
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License
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@ -141,7 +176,6 @@ This project is licensed under the GPLv3 License - see the License_ file for det
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.. |release| image:: https://img.shields.io/pypi/v/nfstream.svg
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:target: https://pypi.python.org/pypi/nfstream
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.. |nfstream_logo| image:: https://github.com/aouinizied/nfstream/blob/master/docs/nfstream_logo.png
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.. |build| image:: https://travis-ci.org/aouinizied/nfstream.svg?branch=master
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:target: https://travis-ci.org/aouinizied/nfstream
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.. |python| image:: https://img.shields.io/badge/python-3.6+-blue.svg
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@ -156,6 +190,10 @@ This project is licensed under the GPLv3 License - see the License_ file for det
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:target: https://travis-ci.org/aouinizied/nfstream
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.. |download| image:: https://img.shields.io/pypi/dm/nfstream.svg
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:target: https://pypistats.org/packages/nfstream
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.. |coverage| image:: https://codecov.io/gh/aouinizied/nfstream/branch/master/graph/badge.svg
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:target: https://codecov.io/gh/aouinizied/nfstream/
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.. |quality| image:: https://img.shields.io/lgtm/grade/python/github/aouinizied/nfstream.svg?logo=lgtm&logoWidth=18)
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:target: https://lgtm.com/projects/g/aouinizied/nfstream/context:python
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.. _License: https://github.com/aouinizied/nfstream/blob/master/LICENSE
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.. _Contributing: https://github.com/aouinizied/nfstream/blob/master/CONTRIBUTING.rst
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@ -9,7 +9,7 @@ Architecture
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A step by step walk through each process involved when performing flow monitoring is
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developed in this section. Our aim is to provide you with a reminder about how
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things works in theory. Consequently, an easier understanding of nfstream features
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things works in theory. Consequently, an easier understanding of NFStream features
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and implementation is possible.
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******************
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@ -17,7 +17,7 @@
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# -- Project information -----------------------------------------------------
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project = 'nfstream'
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project = 'NFStream'
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copyright = '2019, Zied Aouini'
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author = 'Zied Aouini'
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@ -34,8 +34,8 @@ and "help wanted" is open to whoever wants to implement it.
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**Write documentation**
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nfstream could always use more documentation, whether as part of the
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official nfstream docs, in docstrings, or even on the web in blog posts,
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NFStream could always use more documentation, whether as part of the
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official NFStream docs, in docstrings, or even on the web in blog posts,
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articles, and such.
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**Submit feedback**
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@ -53,9 +53,9 @@ If you are proposing a feature:
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Get started
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***********
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Ready to contribute? Here's how to set up nfstream for local development.
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Ready to contribute? Here's how to set up NFStream for local development.
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1. Fork the nfstream repo on GitHub.
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1. Fork the NFStream repo on GitHub.
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2. Clone your fork locally::
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$ git clone git@github.com:your_name_here/nfstream.git
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@ -8,16 +8,16 @@
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:scale: 100%
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:align: left
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########
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######################
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nfstream
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########
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nfstream is a flexible and lightweight network data analysis framework.
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NFStream Documentation
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######################
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NFStream is a flexible and lightweight network data analysis framework.
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**nfstream main features**
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**NFStream main features**
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* **Performance:** nfstream was designed to be fast with a small CPU and memory footprint.
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* **Layer-7 visibility:** nfstream dissection is based on nDPI_ (~300 applications including Tor, Messenger, WhatsApp, etc.).
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* **Performance:** NFStream was designed to be fast with a small CPU and memory footprint.
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* **Layer-7 visibility:** NFStream dissection is based on nDPI_ (~300 applications including Tor, Messenger, WhatsApp, etc.).
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* **Flexibility:** add a flow feature in 2 lines as an NFPlugin_.
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* **Machine Learning oriented:** add your trained model as an NFPlugin_.
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@ -28,7 +28,7 @@ nfstream is a flexible and lightweight network data analysis framework.
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installation
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architecture
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usage
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nfstream
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plugins
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contributing
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changelog
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@ -1,5 +1,5 @@
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###################
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Installing nfstream
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Installing NFStream
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###################
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*************
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@ -25,7 +25,7 @@ Binary installers for the latest released version are available:
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**from source**
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If you want to build nfstream on your local machine:
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If you want to build NFStream on your local machine:
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.. code-block:: bash
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@ -1,10 +1,10 @@
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#####
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Usage
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#####
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########
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NFStream
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########
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Dealing with a big pcap file and just want to aggregate it as network flows?
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nfstream make this path easier in few lines:
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NFStream make this path easier in few lines:
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.. code-block:: python
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@ -88,7 +88,7 @@ NFFlow
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- Last packet timestamp in milliseconds.
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* - nfhash
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- int
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- nfstream hash value for flow (not unique).
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- NFStream hash value for flow (not unique).
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* - version
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- int
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- IP version.
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@ -189,7 +189,7 @@ NFPacket
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- Packet size.
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* - nfhash
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- int
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- nfstream hash value for packet (not unique).
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- NFStream hash value for packet (not unique).
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* - ip_src
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- int
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- Source IP address int value.
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##################
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Extending nfstream
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Extending NFStream
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##################
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nfstream is designed to be flexible and machine learning oriented. In the following section, we depict the use of NFPlugin
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NFStream is designed to be flexible and machine learning oriented. In the following section, we depict the use of NFPlugin
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in both cases.
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.. code-block:: python
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@ -82,5 +82,5 @@ Add trained model prediction
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def process(self, pkt, flow):
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if flow.packets ==3:
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flow.model_prediction = trained_model.predict_proba([flow.feat_1 , flow.feat_2 , flow.feat_3])
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# optionally we can force nfstream to immediately expires the flow
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# optionally we can force NFStreamer to immediately expires the flow
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flow.expiration_id = -1
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