GAR-Project/project

DDoS attacks detection by using SVM on SDN networks.

/ 100

Emerging

This project helps network administrators and security professionals detect Distributed Denial of Service (DDoS) attacks within Software-Defined Networking (SDN) environments. It takes network traffic data from an emulated SDN setup (like Mininet) and uses artificial intelligence to classify incoming traffic, indicating whether it's part of a DDoS attack. This tool is designed for network security engineers or researchers managing SDN infrastructures.

156 stars. No commits in the last 6 months.

Use this if you need to simulate and test DDoS attack detection mechanisms in a controlled Software-Defined Network environment using machine learning.

Not ideal if you are looking for a production-ready DDoS detection system for a live, non-SDN network, or if you prefer a solution without virtualized environments.

DDoS detection SDN security network simulation traffic classification network defense

No License Stale 6m No Package No Dependents

Maintenance 0 / 25

Adoption 10 / 25

Maturity 8 / 25

Community 20 / 25

How are scores calculated?

Stars

156

Forks

Language

Python

License

—

Compare

project and DDoS-attack-detection-and-mitigation-using-deep-neural-network-in-SDN-environment project and DDoS-Detection-SDN

Higher-rated alternatives

santhisenan/DeepDefense

DDoS attack detection using BLSTM based RNN

MeherRushi/FlowSentryX

FlowSentryX is an open-source XDP-based fast packet processing DOS and DDOS Mitigation Framework...

ReubenJoe/DDoS-Detection

Detailed Comparative analysis of DDoS detection using Machine Learning Models

ash0545/sdn-ml-ids

SDN Topology Emulation and Development of Dataset for ML-Based Intrusion Detection through the...

mvoassis/CIC-DDoS2019-DeepLearning

:shield: A GRU deep learning system against attacks in Software Defined Networks (SDN).

Explore ML Frameworks

All categories Trending ML Framework directory Insights