nfstream 1.2.1

A flexible and powerful network data analysis library

nfstream is a flexible and lightweight network data analysis library.

nfstream main features

• Performance: nfstream was designed to be fast with a small CPU and memory footprint.

• Layer-7 visibility: nfstream dissection is based on nDPI (~300 applications including Tor, Messenger, WhatsApp, etc.).

• Flexibility: add a flow metric in 2 lines of code using nfstream plugins method.

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

Use

• 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.streamer import Streamer
my_capture_streamer = Streamer(source="instagram.pcap") # or capture from a network interface
for flow in my_capture_streamer:  # or for flow in my_live_streamer
    print(flow)  # print, append to pandas Dataframe or whatever you want :)!

{"ip_src": "192.168.43.18",
 "src_port": 52066,
 "ip_dst": "66.220.156.68",
 "dst_port": 443,
 "ip_protocol": 6,
 "vlan_id": 0,
 "src_to_dst_pkts": 9,
 "dst_to_src_pkts": 10,
 "src_to_dst_bytes": 1345,
 "dst_to_src_bytes": 4400,
 "syn_count": [1, 1],
 "cwr_count": [0, 0],
 "ece_count": [0, 0],
 "urg_count": [0, 0],
 "ack_count": [8, 10],
 "psh_count": [4, 5],
 "rst_count": [0, 0],
 "fin_count": [0, 0],
 "start_time": 1472393122365.661,
 "end_time": 1472393123665.163,
 "export_reason": 2,
 "metrics": {"application_name": "TLS.Facebook",
             "category_name": "SocialNetwork",
             "http_dns_server_name": "",
             "tls_client_server_name": "facebook.com",
             "tls_server_server_name": "*.facebook.com",
             "tls_server_organization": "Facebook, Inc.",
             "tls_version": "TLSv1.2",
             "tls_not_before": "2014-08-28 00:00:00+00:00",
             "tls_not_after": "2016-12-30 12:00:00+00:00"
             }
 }

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

def my_awesome_plugin(packet_information, flow, direction):
 if packet_information.length > 666:
     return flow.metrics['count_pkts_gt_666'] + 1

streamer_awesome = Streamer(source='devil.pcap', user_metrics={'count_pkts_gt_666': my_awesome_plugin})
for export in streamer_awesome:
   print(export.metrics['count_pkts_gt_666']) # now you will see your 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
# move to nfstream directory and run
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.

License

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