nfstream is a Python package providing fast, flexible, and expressive data structures designed to make working with online or offline network data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world network data analysis in Python. Additionally, it has the broader goal of becoming a common network data processing framework for researchers providing data reproducibility across experiments.
- Performance: nfstream is designed to be fast (x10 faster with pypy3 support) with a small CPU and memory footprint.
- Layer-7 visibility: nfstream deep packet inspection engine is based on [nDPI][ndpi]. It allows nfstream to perform [reliable][reliable] encrypted applications identification and metadata extraction (e.g. TLS, QUIC, TOR, HTTP, SSH, DNS).
- Flexibility: add a flow feature in 2 lines as an [NFPlugin][nfplugin].
- Machine Learning oriented: add your trained model as an [NFPlugin][nfplugin].
How 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 network interface (source="eth0") for flow in my_awesome_streamer: print(flow) # print it, append to pandas Dataframe or whatever you want :)!
NFEntry( id=0, first_seen=1472393122365, last_seen=1472393123665, version=4, src_port=52066, dst_port=443, protocol=6, vlan_id=0, src_ip='192.168.43.18', dst_ip='18.104.22.168', 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' )
- From pcap to Pandas DataFrame?
import pandas as pd streamer_awesome = NFStreamer(source='devil.pcap') data =  for flow in streamer_awesome: data.append(flow.to_namedtuple()) my_df = pd.DataFrame(data=data) my_df.head(5) # Enjoy!
- 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 on_update(self, obs, entry): if obs.length >= 666: entry.my_awesome_plugin += 1 streamer_awesome = NFStreamer(source='devil.pcap', plugins=[my_awesome_plugin()]) for flow in streamer_awesome: print(flow.my_awesome_plugin) # see your dynamically created metric in generated flows
- More example and details are provided on the official [documentation][documentation].
apt-get install libpcap-dev
Binary installers for the latest released version are available:
pip3 install nfstream
Build from source
If you want to build nfstream on your local machine:
git clone https://github.com/aouinizied/nfstream.git cd nfstream python3 setup.py install