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![NFStream Logo](https://raw.githubusercontent.com/nfstream/nfstream/master/assets/nfstream_header_logo.png?raw=true)

--------------------------------------------------------------------------------
[**NFStream**][repo] is a multiplatform Python framework providing fast, flexible, and expressive data structures designed to make
working with **online** or **offline** network data easy and intuitive. It aims to be Python's fundamental high-level
building block for doing practical, **real-world** network flow data analysis. Additionally, it has the broader
goal of becoming **a unifying network data analytics framework for researchers** providing data reproducibility
[**NFStream**][repo] is a multiplatform Python framework providing fast, flexible, and expressive data structures designed to make
working with **online** or **offline** network data easy and intuitive. It aims to be Python's fundamental high-level
building block for doing practical, **real-world** network flow data analysis. Additionally, it has the broader
goal of becoming **a unifying network data analytics framework for researchers** providing data reproducibility
across experiments.

<table>
Expand Down Expand Up @@ -72,12 +72,6 @@ across experiments.
<a href="https://github.com/nfstream/nfstream/actions/workflows/build_test_windows.yml">
<img src="https://img.shields.io/github/actions/workflow/status/nfstream/nfstream/build_test_windows.yml?branch=master&logo=windows&style=for-the-badge&label=windows" alt="Windows WorkFlows" />
</a>
<a href="https://github.com/nfstream/nfstream/actions/workflows/build_test_aarch64.yml">
<img src="https://img.shields.io/github/actions/workflow/status/nfstream/nfstream/build_test_aarch64.yml?branch=master&logo=arm&style=for-the-badge&label=arm64" alt="ARM64 WorkFlows" />
</a>
<a href="https://github.com/nfstream/nfstream/actions/workflows/build_test_armhf.yml">
<img src="https://img.shields.io/github/actions/workflow/status/nfstream/nfstream/build_test_armhf.yml?branch=master&logo=arm&style=for-the-badge&label=arm32" alt="ARM32 WorkFlows" />
</a>
</td>
</tr>
<tr>
Expand All @@ -98,47 +92,47 @@ across experiments.

## 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)
* [**System visibility**](#system-visibility)
* [**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)
* [**Citation**](#citation)
* [**Authors**](#authors)
* [**Supporting organizations**](#supporting-organizations)
* [**Publications that use NFStream**](#publications-that-use-nfstream)
* [**License**](#license)
* [**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)
* [**System visibility**](#system-visibility)
* [**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)
* [**Citation**](#citation)
* [**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_PACKET_V3/FANOUT**][packet] on Linux, multiprocessing, native
[**CFFI based**][cffi] computation engine, and [**PyPy**][pypy] full 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
* **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).
* **System visibility:** NFStream probes the monitored system's kernel to obtain information on open Internet sockets
* **System visibility:** NFStream probes the monitored system's kernel to obtain information on open Internet sockets
and collects guaranteed ground-truth (process name, PID, etc.) at the application level.
* **Statistical features extraction:** NFStream provides state of the art of flow-based statistical feature extraction.
It includes post-mortem statistical features (e.g., minimum, mean, standard deviation, and maximum of packet size and
* **Statistical features extraction:** NFStream provides state of the art of flow-based statistical feature extraction.
It includes post-mortem statistical features (e.g., minimum, mean, standard deviation, and maximum 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 the creation of a new flow
* **Flexibility:** NFStream is easily extensible using [**NFPlugins**][nfplugin]. It allows the creation of a new flow
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 networks using [**NFPlugins**][nfplugin].
* **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 networks using [**NFPlugins**][nfplugin].

## How to get it?

Expand All @@ -147,7 +141,8 @@ Binary installers for the latest released version are available on Pypi.
```bash
pip install nfstream
```
> **Windows Notes**: NFStream does not include capture drivers on Windows (license restrictions). It is required to

> **Windows Notes**: NFStream does not include capture drivers on Windows (license restrictions). It is required to
> install [Npcap drivers][npcap] before installing NFStream.
> If Wireshark is already installed on Windows, then Npcap drivers are already installed, and you do not need to perform
> any additional action.
Expand All @@ -156,7 +151,7 @@ pip install nfstream

### Encrypted application identification and metadata extraction

Dealing with a big pcap file and want to aggregate into labeled network flows? **NFStream** make this path easier in
Dealing with a big pcap file and want to aggregate into labeled network flows? **NFStream** make this path easier in
a few lines:

```python
Expand Down Expand Up @@ -231,7 +226,7 @@ NFlow(id=0,

### System visibility

NFStream probes the monitored system's kernel to obtain information on open Internet sockets and collects guaranteed
NFStream probes the monitored system's kernel to obtain information on open Internet sockets and collects guaranteed
ground-truth (process name, PID, etc.) at the application level.

```python
Expand Down Expand Up @@ -285,7 +280,7 @@ NFlow(id=0,

### Post-mortem statistical flow features extraction

NFStream performs 48 post-mortem flow statistical features extraction, which includes detailed TCP flags analysis,
NFStream performs 48 post-mortem flow statistical features extraction, which includes detailed TCP flags analysis,
minimum, mean, maximum, and standard deviation of both packet size and inter-arrival time in each direction.

```python
Expand Down Expand Up @@ -381,7 +376,8 @@ NFlow(id=0,
```

### Early statistical flow features extraction
NFStream performs early (up to 255 packets) flow statistical features extraction (referred to as SPLT analysis in the

NFStream performs early (up to 255 packets) flow statistical features extraction (referred to as SPLT analysis in the
literature). It is summarized as a sequence of these packets' directions, sizes, and inter-arrival times.

```python
Expand Down Expand Up @@ -453,7 +449,6 @@ 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 an export interface.
Expand Down Expand Up @@ -481,7 +476,7 @@ class MyCustomPktSizeFeature(NFPlugin):
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:
Expand All @@ -498,9 +493,9 @@ for flow in extended_streamer:

### Machine Learning models training and deployment

The following simplistic example demonstrates how to train and deploy a machine-learning approach for traffic
flow categorization.
We want to run a classification of Social Network category flows based on bidirectional_packets and bidirectional_bytes
The following simplistic example demonstrates how to train and deploy a machine-learning approach for traffic
flow categorization.
We want to run a classification of Social Network category flows based on bidirectional_packets and bidirectional_bytes
as input features. For the sake of brevity, we decide to predict only at the flow expiration stage.

#### Training the model
Expand Down Expand Up @@ -534,12 +529,12 @@ for flow in ml_streamer:
print(flow.udps.model_prediction)
```

More NFPlugin examples and details are provided in the official [**documentation**][documentation]. You can also test
More NFPlugin examples and details are provided in the official [**documentation**][documentation]. You can also test
NFStream without installation using our [**live demo notebook**][demo].

## Building from sources ![l] ![m] ![w]
## Building from sources ![l] ![m] ![w]

To build **NFStream** from sources, please read the [**installation guide**][install] provided in the official
To build **NFStream** from sources, please read the [**installation guide**][install] provided in the official
documentation.

## Contributing
Expand All @@ -549,16 +544,16 @@ requests to us.

## Ethics

**NFStream** is intended for network data research and forensics. Researchers and network data scientists can use this
framework to build reliable datasets and train and evaluate network-applied machine learning models.
As with any packet monitoring tool, **NFStream** could be misused. **Do not run it on any network that you do not own or
**NFStream** is intended for network data research and forensics. Researchers and network data scientists can use this
framework to build reliable datasets and train and evaluate network-applied machine learning models.
As with any packet monitoring tool, **NFStream** could be misused. **Do not run it on any network that you do not own or
administrate**.

## Credits

### Citation

[**NFStream paper**][doi] is published in [**Computer Networks (COMNET)**][comnet]. If you use NFStream in a scientific
[**NFStream paper**][doi] is published in [**Computer Networks (COMNET)**][comnet]. If you use NFStream in a scientific
publication, we would appreciate citations to the following article:

``` latex
Expand All @@ -579,6 +574,7 @@ publication, we would appreciate citations to the following article:
### Authors

The following people contributed to NFStream:

* [**Zied Aouini**](mailto:[email protected]): Creator and core developer.
* [**Adrian Pekar**](mailto:[email protected]): Datasets generation and storage.
* [**Romain Picard**](mailto:[email protected]): MDNS and DHCP plugins implementation.
Expand All @@ -587,13 +583,14 @@ The following people contributed to NFStream:
### Supporting organizations

The following organizations supported NFStream:

* [**SoftAtHome**](https://www.softathome.com/): Supporter of NFStream development.
* [**Technical University of Košice**](https://www.tuke.sk/): Hardware and infrastructure for datasets generation and
* [**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**][ndpi] integration.
* [**The Nmap Project**](https://nmap.org): Technical support of [**Npcap**][npcap] integration
* [**The Nmap Project**](https://nmap.org): Technical support of [**Npcap**][npcap] integration
(NPCAP OEM installer on Windows CI).
* [**Google OSS Fuzz**](https://google.github.io/oss-fuzz/): Continious fuzzing
* [**Google OSS Fuzz**](https://google.github.io/oss-fuzz/): Continious fuzzing
testing support of NFStream project.

[![sah]](https://www.softathome.com/) [![tuke]](https://www.tuke.sk/) [![ntop]](https://www.ntop.org/) [![nmap]](https://nmap.org/) [![google]](https://google.github.io/oss-fuzz/)
Expand Down Expand Up @@ -625,7 +622,6 @@ This project is licensed under the LGPLv3 License - see the [**License**][licens

[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
[npcap]: https://npcap.org
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