Nfstream

Abstract

1. Introduction

[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.

It allows NFStream to perform [reliable][reliable] encrypted applications identification and metadata fingerprinting (e.g. TLS, SSH, DHCP, HTTP).

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].

2. Objective

NFStream: a Flexible Network Data Analysis Framework.

This project demonstrates how Python, Machine Learning can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Performance: NFStream is designed to be fast: [AF_PACKET_V3/FANOUT][packet] on Linux, multiprocessing, native
• Encrypted layer-7 visibility: NFStream deep packet inspection is based on [nDPI][ndpi].
• System visibility: NFStream probes the monitored system's kernel to obtain information on open Internet sockets
• Statistical features extraction: NFStream provides state of the art of flow-based statistical feature extraction.
• Flexibility: NFStream is easily extensible using [NFPlugins][nfplugin]. It allows the creation of a new flow
• Machine Learning oriented: NFStream aims to make Machine Learning Approaches for network traffic management

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/nfstream/nfstream.git
cd nfstream

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by nfstream and published on GitHub under the GNU Lesser General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.