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![NFStream Logo](https://raw.githubusercontent.com/nfstream/nfstream/master/assets/nfstream_header_logo.png?raw=true)
--------------------------------------------------------------------------------
[**NFStream**][repo] is a Python framework 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 analytics framework for researchers** providing data reproducibility
across experiments.
<table>
<tr>
<td><b>Live Notebook</b></td>
<td>
<a href="https://mybinder.org/v2/gh/nfstream/nfstream-tutorials/master?filepath=demo_notebook.ipynb">
<img src="https://img.shields.io/badge/notebook-launch-blue?logo=jupyter&style=for-the-badge" alt="live notebook" />
</a>
</td>
</tr>
<tr>
<td><b>Project Website</b></td>
<td>
<a href="https://www.nfstream.org/">
<img src="https://img.shields.io/website?down_color=red&down_message=down&label=nfstream.org&logo=github&up_color=blue&up_message=up&url=https%3A%2F%2Fnfstream.org%2F&style=for-the-badge" alt="website" />
</a>
</td>
</tr>
<tr>
<td><b>Discussion Channel</b></td>
<td>
<a href="https://gitter.im/nfstream/community">
<img src="https://img.shields.io/badge/chat-on%20gitter-blue?color=blue&logo=gitter&style=for-the-badge" alt="Gitter" />
</a>
</td>
</tr>
<tr>
<td><b>Latest Release</b></td>
<td>
<a href="https://pypi.python.org/pypi/nfstream">
<img src="https://img.shields.io/pypi/v/nfstream.svg?logo=pypi&style=for-the-badge" alt="latest release" />
</a>
</td>
</tr>
<tr>
<td><b>Supported Versions</b></td>
<td>
<a href="https://github.com/nfstream/nfstream/actions?query=workflow%3Abuild">
<img src="https://img.shields.io/pypi/pyversions/nfstream?logo=python&style=for-the-badge" alt="python3" />
</a>
<a href="https://github.com/nfstream/nfstream/actions?query=workflow%3Abuild">
<img src="https://img.shields.io/badge/pypy-3.6%20%7C%203.7-blue?logo=pypy&style=for-the-badge" alt="pypy3" />
</a>
</td>
</tr>
<tr>
<td><b>Project License</b></td>
<td>
<a href="https://github.com/nfstream/nfstream/blob/master/LICENSE">
<img src="https://img.shields.io/pypi/l/nfstream?logo=gnu&style=for-the-badge&color=blue" alt="License" />
</a>
</td>
</tr>
<tr>
<td><b>Continuous Integration</b></td>
<td>
<a href="https://github.com/nfstream/nfstream/actions?query=workflow%3Abuild_test_publish_linux">
<img src="https://img.shields.io/github/workflow/status/nfstream/nfstream/build_test_publish_linux/master?logo=linux&style=for-the-badge&label=linux" alt="Linux WorkFlows" />
</a>
<a href="https://github.com/nfstream/nfstream/actions?query=workflow%3Abuild_test_publish_macos">
<img src="https://img.shields.io/github/workflow/status/nfstream/nfstream/build_test_publish_macos/master?logo=apple&style=for-the-badge&label=macos" alt="MacOS WorkFlows" />
</a>
<a href="https://github.com/nfstream/nfstream/actions?query=workflow%3Abuild_test_publish_windows">
<img src="https://img.shields.io/github/workflow/status/nfstream/nfstream/build_test_publish_windows/master?logo=windows&style=for-the-badge&label=windows" alt="Windows WorkFlows" />
</a>
</td>
</tr>
<tr>
<td><b>Code Quality</b></td>
<td>
<a href="https://lgtm.com/projects/g/nfstream/nfstream/context:python">
<img src="https://img.shields.io/lgtm/grade/python/github/nfstream/nfstream?label=LGTM%3A%20Python&logo=lgtm&style=for-the-badge&logoWidth=18)" alt="Quality" />
</a>
<a href="https://www.codefactor.io/repository/github/nfstream/nfstream">
<img src="https://img.shields.io/codefactor/grade/github/nfstream/nfstream?label=codefactor%3A%20Python%2C%20C&logo=codefactor&style=for-the-badge&logoWidth=18)" alt="Quality" />
</a>
</td>
</tr>
<tr>
<td><b>Code Coverage</b></td>
<td>
<a href="https://codecov.io/gh/nfstream/nfstream/">
<img src="https://img.shields.io/codecov/c/github/nfstream/nfstream?color=brightgreen&logo=codecov&style=for-the-badge" alt="Coverage" />
</a>
</td>
</tr>
</table>
## Table of Contents
* [**Main Features**](#main-features)
* [**How to get it?**](#how-to-get-it)
* [**How to use it?**](#how-to-use-it)
* [**Encrypted application identification and metadata extraction**](#encrypted-application-identification-and-metadata-extraction)
* [**Post-mortem statistical flow features extraction**](#post-mortem-statistical-flow-features-extraction)
* [**Early statistical flow features extraction**](#early-statistical-flow-features-extraction)
* [**Pandas export interface**](#pandas-export-interface)
* [**CSV export interface**](#csv-export-interface)
* [**Extending NFStream**](#extending-nfstream)
* [**Machine Learning models training and deployment**](#machine-learning-models-training-and-deployment)
* [**Training the model**](#training-the-model)
* [**ML powered streamer on live traffic**](#ml-powered-streamer-on-live-traffic)
* [**Building from sources**](#building-from-sources--)
* [**Contributing**](#contributing)
* [**Ethics**](#ethics)
* [**Credits**](#credits)
* [**Authors**](#authors)
* [**Supporting organizations**](#supporting-organizations)
* [**Publications that use NFStream**](#publications-that-use-nfstream)
* [**License**](#license)
## Main Features
* **Performance:** NFStream is designed to be fast: AF_PACKETV3/FANOUT on Linux, parallel processing, native C
(using [**CFFI**][cffi]) for critical computation and [**PyPy**][pypy] support.
* **Encrypted layer-7 visibility:** NFStream deep packet inspection is based on [**nDPI**][ndpi].
It allows NFStream to perform [**reliable**][reliable] encrypted applications identification and metadata
fingerprinting (e.g. TLS, SSH, DHCP, HTTP).
* **Statistical features extraction:** NFStream provides state of the art of flow-based statistical feature extraction.
It includes both post-mortem statistical features (e.g. min, mean, stddev and max 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**][nfplugin]. It allows to create a new
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 network using [**NFPlugins**][nfplugin].
## How to get it?
Binary installers for the latest released version are available on Pypi.
```bash
pip install nfstream
```
## How to use it?
### Encrypted application identification and metadata extraction
Dealing with a big pcap file and just want to aggregate into labeled network flows? **NFStream** make this path easier
in few lines:
```python
from nfstream import NFStreamer
# We display all streamer parameters with their default values.
# See documentation for detailed information about each parameter.
# https://www.nfstream.org/docs/api#nfstreamer
my_streamer = NFStreamer(source="facebook.pcap", # or network interface
decode_tunnels=True,
bpf_filter=None,
promiscuous_mode=True,
snapshot_length=1536,
idle_timeout=120,
active_timeout=1800,
accounting_mode=0,
udps=None,
n_dissections=20,
statistical_analysis=False,
splt_analysis=0,
n_meters=0,
performance_report=0)
for flow in my_streamer:
print(flow) # print it.
```
```python
# See documentation for each feature detailed description.
# https://www.nfstream.org/docs/api#nflow
NFlow(id=0,
expiration_id=0,
src_ip='192.168.43.18',
src_mac='30:52:cb:6c:9c:1b',
src_oui='30:52:cb',
src_port=52066,
dst_ip='66.220.156.68',
dst_mac='98:0c:82:d3:3c:7c',
dst_oui='98:0c:82',
dst_port=443,
protocol=6,
ip_version=4,
vlan_id=0,
tunnel_id=0,
bidirectional_first_seen_ms=1472393122365,
bidirectional_last_seen_ms=1472393123665,
bidirectional_duration_ms=1300,
bidirectional_packets=19,
bidirectional_bytes=5745,
src2dst_first_seen_ms=1472393122365,
src2dst_last_seen_ms=1472393123408,
src2dst_duration_ms=1043,
src2dst_packets=9,
src2dst_bytes=1345,
dst2src_first_seen_ms=1472393122668,
dst2src_last_seen_ms=1472393123665,
dst2src_duration_ms=997,
dst2src_packets=10,
dst2src_bytes=4400,
application_name='TLS.Facebook',
application_category_name='SocialNetwork',
application_is_guessed=0,
requested_server_name='facebook.com',
client_fingerprint='bfcc1a3891601edb4f137ab7ab25b840',
server_fingerprint='2d1eb5817ece335c24904f516ad5da12',
user_agent='',
content_type='')
```
### Post-mortem statistical flow features extraction
NFStream performs 48 post mortem flow statistical features extraction which include detailed TCP flags analysis,
minimum, mean, maximum and standard deviation of both packet size and interarrival time in each direction.
```python
from nfstream import NFStreamer
my_streamer = NFStreamer(source="facebook.pcap",
# Disable L7 dissection for readability purpose.
n_dissections=0,
statistical_analysis=True)
for flow in my_streamer:
print(flow)
```
```python
# See documentation for each feature detailed description.
# https://www.nfstream.org/docs/api#nflow
NFlow(id=0,
expiration_id=0,
src_ip='192.168.43.18',
src_mac='30:52:cb:6c:9c:1b',
src_oui='30:52:cb',
src_port=52066,
dst_ip='66.220.156.68',
dst_mac='98:0c:82:d3:3c:7c',
dst_oui='98:0c:82',
dst_port=443,
protocol=6,
ip_version=4,
vlan_id=0,
tunnel_id=0,
bidirectional_first_seen_ms=1472393122365,
bidirectional_last_seen_ms=1472393123665,
bidirectional_duration_ms=1300,
bidirectional_packets=19,
bidirectional_bytes=5745,
src2dst_first_seen_ms=1472393122365,
src2dst_last_seen_ms=1472393123408,
src2dst_duration_ms=1043,
src2dst_packets=9,
src2dst_bytes=1345,
dst2src_first_seen_ms=1472393122668,
dst2src_last_seen_ms=1472393123665,
dst2src_duration_ms=997,
dst2src_packets=10,
dst2src_bytes=4400,
bidirectional_min_ps=66,
bidirectional_mean_ps=302.36842105263156,
bidirectional_stddev_ps=425.53315715259754,
bidirectional_max_ps=1454,
src2dst_min_ps=66,
src2dst_mean_ps=149.44444444444446,
src2dst_stddev_ps=132.20354676701294,
src2dst_max_ps=449,
dst2src_min_ps=66,
dst2src_mean_ps=440.0,
dst2src_stddev_ps=549.7164925870628,
dst2src_max_ps=1454,
bidirectional_min_piat_ms=0,
bidirectional_mean_piat_ms=72.22222222222223,
bidirectional_stddev_piat_ms=137.34994188549086,
bidirectional_max_piat_ms=398,
src2dst_min_piat_ms=0,
src2dst_mean_piat_ms=130.375,
src2dst_stddev_piat_ms=179.72036811192467,
src2dst_max_piat_ms=415,
dst2src_min_piat_ms=0,
dst2src_mean_piat_ms=110.77777777777777,
dst2src_stddev_piat_ms=169.51458475436397,
dst2src_max_piat_ms=409,
bidirectional_syn_packets=2,
bidirectional_cwr_packets=0,
bidirectional_ece_packets=0,
bidirectional_urg_packets=0,
bidirectional_ack_packets=18,
bidirectional_psh_packets=9,
bidirectional_rst_packets=0,
bidirectional_fin_packets=0,
src2dst_syn_packets=1,
src2dst_cwr_packets=0,
src2dst_ece_packets=0,
src2dst_urg_packets=0,
src2dst_ack_packets=8,
src2dst_psh_packets=4,
src2dst_rst_packets=0,
src2dst_fin_packets=0,
dst2src_syn_packets=1,
dst2src_cwr_packets=0,
dst2src_ece_packets=0,
dst2src_urg_packets=0,
dst2src_ack_packets=10,
dst2src_psh_packets=5,
dst2src_rst_packets=0,
dst2src_fin_packets=0)
```
### Early statistical flow features extraction
NFStream performs early (up to 255 packets) flow statistical features extraction (also referred as SPLT analysis in the
literature). It is summarized as a sequence a these packets directions, sizes and interarrival times.
```python
from nfstream import NFStreamer
my_streamer = NFStreamer(source="facebook.pcap",
# We disable both l7 dissection and statistical analysis
# for readability purpose.
n_dissections=0,
statistical_analysis=False,
splt_analysis=10)
for flow in my_streamer:
print(flow)
```
```python
# See documentation for each feature detailed description.
# https://www.nfstream.org/docs/api#nflow
NFlow(id=0,
expiration_id=0,
src_ip='192.168.43.18',
src_mac='30:52:cb:6c:9c:1b',
src_oui='30:52:cb',
src_port=52066,
dst_ip='66.220.156.68',
dst_mac='98:0c:82:d3:3c:7c',
dst_oui='98:0c:82',
dst_port=443,
protocol=6,
ip_version=4,
vlan_id=0,
tunnel_id=0,
bidirectional_first_seen_ms=1472393122365,
bidirectional_last_seen_ms=1472393123665,
bidirectional_duration_ms=1300,
bidirectional_packets=19,
bidirectional_bytes=5745,
src2dst_first_seen_ms=1472393122365,
src2dst_last_seen_ms=1472393123408,
src2dst_duration_ms=1043,
src2dst_packets=9,
src2dst_bytes=1345,
dst2src_first_seen_ms=1472393122668,
dst2src_last_seen_ms=1472393123665,
dst2src_duration_ms=997,
dst2src_packets=10,
dst2src_bytes=4400,
# The sequence of 10 first packet direction, size and inter arrival time.
splt_direction=[0, 1, 0, 0, 1, 1, 0, 1, 0, 1],
splt_ps=[74, 74, 66, 262, 66, 1454, 66, 1454, 66, 463],
splt_piat_ms=[0, 303, 0, 0, 313, 0, 0, 0, 0, 1])
```
### Pandas export interface
NFStream natively supports Pandas as export interface.
```python
# See documentation for more details.
# https://www.nfstream.org/docs/api#pandas-dataframe-conversion
from nfstream import NFStreamer
my_dataframe = NFStreamer(source='teams.pcap').to_pandas()[["src_ip",
"src_port",
"dst_ip",
"dst_port",
"protocol",
"bidirectional_packets",
"bidirectional_bytes",
"application_name"]]
my_dataframe.head(5)
```
![Pandas](https://raw.githubusercontent.com/nfstream/nfstream/master/assets/pandas_df.png?raw=true)
### CSV export interface
NFStream natively supports CSV file format as export interface.
```python
# See documentation for more details.
# https://www.nfstream.org/docs/api#csv-file-conversion
flows_count = NFStreamer(source='facebook.pcap').to_csv(path=None,
flows_per_file=0,
columns_to_anonymize=[])
```
### Extending NFStream
Didn't find a specific flow feature? add a plugin to **NFStream** in few lines:
```python
from nfstream import NFPlugin
class MyCustomFeature(NFPlugin):
def on_init(self, packet, flow):
# flow creation with the first packet
if packet.raw_size == self.custom_size:
flow.udps.packet_with_custom_size = 1
else:
flow.udps.packet_with_custom_size = 0
def on_update(self, packet, flow):
# flow update with each packet belonging to the flow
if packet.raw_size == self.custom_size:
flow.udps.packet_with_custom_size += 1
extended_streamer = NFStreamer(source='facebook.pcap',
udps=MyCustomFeature(custom_size=555))
for flow in extended_streamer:
# see your dynamically created metric in generated flows
print(flow.udps.packet_with_custom_size)
```
### Machine Learning models training and deployment
In the following example, we demonstrate a simplistic machine learning approach training and deployment.
We suppose that we want to run a classification of Social Network category flows based on bidirectional_packets and
bidirectional_bytes as features. For the sake of brevity, we decide to predict only at flow expiration stage.
#### Training the model
```python
from nfstream import NFPlugin, NFStreamer
import numpy
from sklearn.ensemble import RandomForestClassifier
df = NFStreamer(source="training_traffic.pcap").to_pandas()
X = df[["bidirectional_packets", "bidirectional_bytes"]]
y = df["application_category_name"].apply(lambda x: 1 if 'SocialNetwork' in x else 0)
model = RandomForestClassifier()
model.fit(X, y)
```
#### ML powered streamer on live traffic
```python
class ModelPrediction(NFPlugin):
def on_init(self, packet, flow):
flow.udps.model_prediction = 0
def on_expire(self, flow):
# You can do the same in on_update entrypoint and force expiration with custom id.
to_predict = numpy.array([flow.bidirectional_packets,
flow.bidirectional_bytes]).reshape((1,-1))
flow.udps.model_prediction = self.my_model.predict(to_predict)
ml_streamer = NFStreamer(source="eth0", udps=ModelPrediction(my_model=model))
for flow in ml_streamer:
print(flow.udps.model_prediction)
```
More NFPlugin examples and details are provided on the official [**documentation**][documentation]. You can also test
NFStream without installation using our [**live demo notebook**][demo].
## Building from sources ![l] ![m]
If you want to build **NFStream** from sources. Please read the [**installation guide**][install].
## Contributing
Please read [**Contributing**][contribute] for details on our code of conduct, and the process for submitting pull
requests to us.
## 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**.
## Credits
### Authors
The following people contributed to NFStream:
* [**Zied Aouini**](mailto:aouinizied@gmail.com): Creator and main developer.
* [**Adrian Pekar**](mailto:adrian.pekar@gmail.com): Testing, datasets generation and storage.
* [**Romain Picard**](mailto:romain.picard@oakbits.com): Several Plugins implementation.
* [**Radion Bikmukhamedov**](mailto:radion.bikmukhamedov@pm.me): Initial work on SPLT analysis NFPlugin.
### Supporting organizations
The following organizations are supporting NFStream:
* [**SoftAtHome**](https://www.softathome.com/): Main supporter of NFStream development.
* [**Technical University of Košice**](https://www.tuke.sk/): Hardware and infrastructure for datasets generation and
storage.
* [**ntop**](https://www.ntop.org/): Technical support of nDPI integration.
[![sah]](https://www.softathome.com/) [![tuke]](https://www.tuke.sk/) [![ntop]](https://www.ntop.org/)
## Publications that use NFStream
* [**Ranking Network Devices for Alarm Prioritisation: Intrusion Detection Case Study**](https://ieeexplore.ieee.org/abstract/document/9559086)
* [**Network Flows-Based Malware Detection Using A Combined Approach of Crawling And Deep Learning**](https://ieeexplore.ieee.org/document/9500920)
* [**Generative Transformer Framework For Network Traffic Generation And Classification**](https://cyberleninka.ru/article/n/generative-transformer-framework-for-network-traffic-generation-and-classification)
* [**Multi-Class Network Traffic Generators and Classifiers Based on Neural Networks**](https://ieeexplore.ieee.org/document/9416067)
* [**Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1 A New IoT Dataset**](https://www.mdpi.com/1424-8220/21/14/4834)
* [**An Approach Based on Knowledge-Defined Networking for Identifying Video Streaming Flows in 5G Networks**](https://latamt.ieeer9.org/index.php/transactions/article/view/5083/1116)
* [**Knowledge Discovery: Can It Shed New Light on Threshold Definition for Heavy‑Hitter Detection?**](https://link.springer.com/content/pdf/10.1007/s10922-021-09593-w.pdf)
* [**Collecting and analyzing Tor exit node traffic**](https://www.diva-portal.org/smash/get/diva2:1575255/FULLTEXT01.pdf)
## License
This project is licensed under the LGPLv3 License - see the [**License**][license] file for details
[license]: https://github.com/nfstream/nfstream/blob/master/LICENSE
[contribute]: https://nfstream.org/docs/community
[contributors]: https://github.com/nfstream/nfstream/graphs/contributors
[documentation]: https://nfstream.org/
[ndpi]: https://github.com/ntop/nDPI
[nfplugin]: https://nfstream.org/docs/api#nfplugin
[reliable]: http://people.ac.upc.edu/pbarlet/papers/ground-truth.pam2014.pdf
[repo]: https://nfstream.org/
[demo]: https://mybinder.org/v2/gh/nfstream/nfstream/master?filepath=demo_notebook.ipynb
[pypy]: https://www.pypy.org/
[cffi]: https://cffi.readthedocs.io/en/latest/index.html
[sah]:https://raw.githubusercontent.com/nfstream/nfstream/master/assets/sah_logo.png?raw=true
[tuke]:https://raw.githubusercontent.com/nfstream/nfstream/master/assets/tuke_logo.png?raw=true
[ntop]:https://raw.githubusercontent.com/nfstream/nfstream/master/assets/ntop_logo.png?raw=true
[l]:https://github.com/ryanoasis/nerd-fonts/wiki/screenshots/v1.0.x/linux-pass-sm.png
[m]:https://github.com/ryanoasis/nerd-fonts/wiki/screenshots/v1.0.x/mac-pass-sm.png
[install]: https://www.nfstream.org/docs/#building-nfstream-from-sources
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