mininet

Emulator for rapid prototyping of Software Defined Networks

Mininet fork adding support for container-based (e.g. Docker) emulated hosts.

SDN 學習及實作範例。(因個人職涯關係，已不再維護，請見諒。)

SDN networks (Software Defined Networking ) are exposed to new security threats and attacks, especially Distributed Denial of Service (DDoS) attacks. For this aim, we have proposed a model able to detect and mitigate attacks automatically in SDN networks using Machine Learning (ML)

MiniCPS: a framework for Cyber-Physical Systems real-time simulation, built on top of mininet

DDoS attacks detection by using SVM on SDN networks.

Fast creation and configuration of topologies, traffic matrices and event schedules for network experiments

An attempt to detect and prevent DDoS attacks using reinforcement learning. The simulation was done using Mininet.

Testbed/Tutorials/Researching work on P4.

Creates a simple Ryu app using the tutorials and then adds on to it.

A Go testing framework for distributed applications

Mininet-based NDN emulator (mailing list: https://www.lists.cs.ucla.edu/mailman/listinfo/mini-ndn)

SDN topology editor in your web browser with Mininet, image and addressing plan export.

A Network Animator for Visualizing Real-Time Packet Flows in Mininet

Applying Machine Learning model (SVM) into DDoS attack detection in SDN.

A system that could classify DNS, Telnet, Ping, Voice, Game, and Video traffic flows based on packet and byte information simulated by the Distributed Internet Traffic Generator (D-ITG) tool in an Software Defined Network (SDN) based network topology with Open vSwitch (OVS) using machine learning algorithms such as Logistic regression,K-Means clustering,K nearest neighbours, SVC, Gaussian NB and Random Forest Classifier.

Attention! Legacy! This repo will be replaced with https://github.com/containernet/vim-emu

Mitigation and Detection of DDoS Attacks in Software Defined Networks

Learning SDN within Ryu controller and SDN experiments/以Ryu作为控制器的SDN学习以及实验

BPFabric implementations. Details about this work are available in the research paper "BPFabric: Data Plane Programmability for Software Defined Networks" published at ANCS 2017