Main Features
- Performance:nfstream is designed to be fast (x10 faster with pypy3 support) with a small CPU and memory footprint.
- Layer-7 visibility:nfstreamdeep packet inspection engine is based on nDPI. It allows nfstream to perform 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.
- Machine Learning oriented: add your trained model as an 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='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'
)- 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.
Prerequisites
apt-get install libpcap-devInstallation
Using pip
Binary installers for the latest released version are available:
pip3 install nfstreamBuild 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 installContributing
Please read Contributing for details on our code of conduct, and the process for submitting pull requests to us.
Authors
Zied Aouini 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.