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Getting Started

NFStream is a multiplatform Python framework providing fast, flexible, and expressive data structures designed to make working with online or offline network data easy and intuitive. It aims to be Python’s fundamental high-level building block for doing practical, real-world network flow data analysis. Additionally, it has the broader goal of becoming a unifying network data analytics framework for researchers providing data reproducibility across experiments.

Table of contents

  1. Main Features
  2. Installation Guide
    1. Python packages manager
    2. Building NFStream from sources
      1. Linux Prerequisites
      2. MacOS Prerequisites
    3. Windows Prerequisites
    4. Build NFStream

Main Features

  • Performance: NFStream is designed to be fast: AF_PACKET_V3/FANOUT on Linux, multiprocessing, native CFFI based computation engine, and PyPy full support.
  • Encrypted layer-7 visibility: NFStream deep packet inspection is based on nDPI. It allows NFStream to perform reliable encrypted applications identification and metadata fingerprinting (e.g. TLS, SSH, DHCP, HTTP).
  • System visibility: NFStream probes the monitored system’s kernel to obtain information on open Internet sockets and collects guaranteed ground-truth (process name, PID, etc.) at the application level.
  • Statistical features extraction: NFStream provides state of the art of flow-based statistical feature extraction. It includes post-mortem statistical features (e.g., minimum, mean, standard deviation, and maximum of packet size and inter-arrival time) and early flow features (e.g. sequence of first n packets sizes, inter-arrival times, and directions).
  • Flexibility: NFStream is easily extensible using NFPlugins. It allows the creation of a new flow feature within a few lines of Python.
  • Machine Learning oriented: NFStream aims to make Machine Learning Approaches for network traffic management reproducible and deployable. By using NFStream as a common framework, researchers ensure that models are trained using the same feature computation logic, and thus, a fair comparison is possible. Moreover, trained models can be deployed and evaluated on live networks using NFPlugins.

Installation Guide

Python packages manager

Binary installers for the latest released version are available on Pypi.

pip install nfstream

Building NFStream from sources

Linux Prerequisites

sudo apt-get update
sudo apt-get install python3-dev autoconf automake libtool pkg-config flex bison gettext libjson-c-dev
sudo apt-get install libusb-1.0-0-dev libdbus-glib-1-dev libbluetooth-dev libnl-genl-3-dev

MacOS Prerequisites

brew install autoconf automake libtool pkg-config gettext json-c

Windows Prerequisites

On Windows, NFStream build system is based on MSYS2. Please follow msys2 installation guide before moving to the next steps.

pacman -S git unzip mingw-w64-x86_64-toolchain automake1.16 automake-wrapper autoconf libtool make mingw-w64-x86_64-json-c mingw-w64-x86_64-crt-git

Note that you will also need to have npcap installed according to these instructions.

Build NFStream

git clone --recurse-submodules https://github.com/nfstream/nfstream.git
cd nfstream
python3 -m pip install --upgrade pip
python3 -m pip install -r dev_requirements.txt
python3 -m pip install .