nfstream 2.0.1

A flexible and powerful network data analysis framework

NFStream is a flexible and lightweight network data analysis framework.

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Main Features

• Performance: NFStream is designed to be fast (pypy3 support) with a small CPU and memory footprint.

• Layer-7 visibility: NFStream deep packet inspection engine is based on nDPI library. It allows NFStream to perform reliable encrypted applications identification and metadata extraction (e.g. TLS, SSH, DNS, HTTP).

• Flexibility: add a flow feature in 2 lines as an NFPlugin.

• Machine Learning oriented: add your trained model as an NFPlugin.

When to use it?

• Dealing with a big pcap file and just want to aggregate it as network flows? NFStream make this path easier in few lines:

from nfstream import NFStreamer
my_awesome_streamer = NFStreamer(source="facebook.pcap") # or capture from a network interface (source="eth0")
for flow in my_awesome_streamer:
    print(flow)  # print, append to pandas Dataframe or whatever you want :)!

NFFlow(
    flow_id=0,
    first_seen=1472393122365,
    last_seen=1472393123665,
    nfhash=1456034341,
    version=4,
    src_port=52066,
    dst_port=443,
    protocol=6,
    vlan_id=0,
    src_ip='192.168.43.18',
    dst_ip='66.220.156.68',
    total_packets=19,
    total_bytes=5745,
    duration=1300,
    src2dst_packets=9,
    src2dst_bytes=1345,
    dst2src_packets=10,
    dst2src_bytes=4400,
    expiration_id=0,
    master_protocol=91,
    app_protocol=119,
    application_name='TLS.Facebook',
    category_name='SocialNetwork',
    client_info='facebook.com',
    server_info='*.facebook.com',
    j3a_client='bfcc1a3891601edb4f137ab7ab25b840',
    j3a_server='2d1eb5817ece335c24904f516ad5da12'
)

• Didn’t find a specific flow feature? add a plugin to NFStream in few lines:

 from nfstream import NFPlugin

 class my_awesome_plugin(NFPlugin):
     def process(self, pkt, flow):
         if pkt.length >= 666:
             flow.my_awesome_plugin += 1

streamer_awesome = NFStreamer(source='devil.pcap', plugins=[my_awesome_plugin()])
for flow in streamer_awesome:
   print(flow.my_awesome_plugin) # now you will see your dynamically created metric in generated flows

• More example and details are provided on the official Documentation.

Getting Started

Prerequisites

apt-get install libpcap-dev

Installation

using pip

Binary installers for the latest released version are available:

pip3 install nfstream

from source

If you want to build NFStream on your local machine:

apt-get install autogen
git clone https://github.com/aouinizied/nfstream.git
cd nfstream
python3 setup.py install

Contributing

Please read Contributing for details on our code of conduct, and the process for submitting pull requests to us.

Authors

Zied Aouini (aouinizied) created NFStream and these fine people have contributed.

Ethics

NFStream is intended for network data research and forensics. Researchers and network data scientists can use these framework to build reliable datasets, train and evaluate network applied machine learning models. As with any packet monitoring tool, NFStream could potentially be misused. Do not run it on any network of which you are not the owner or the administrator.

License

This project is licensed under the GPLv3 License - see the License file for details