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)

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

This is my master's thesis project: "QoS implementation in Software Defined Network using Ryu Controller"

DDOS Mitigation System based on Ryu SDN Controller with Port Blocking Mechanism

Implemented a network intrusion detection system for a software defined network using Random Forest method for classification of port and flow statistics.

routing-implementation in SDN based on switch processing delay and remaining bandwidth

This is my study on detection and mitigation DDoS attacks in SDN

This project is created using Ryu controller which performs DIJKSTRA algorithm to find best paths, based on traffic flowing through links.

A prototype for an architecture that uses Machine Learning to detect abnormalities in the SDN network packet flow, built by Ryu SDN and mininet

Implementation of a Software Defined Perimeter in Ryu SDN Framework

Simple network monitor for ryu controller

Systematic Application Layer that Allows Network Monitoring and Firewall Rules for Ryu-based SDN Environments.

An Enhanced Non-dominated Sorting Genetic Algorithm II-based Dynamic multi-objective QoS routing in Software-Defined Networks

PhD Coursework: 21CS637 - Advanced Networks

Final project of "Software Defined Networking" course at Polytechnic University of Milan.

MininetIDS is an integrated environment for developing and evaluating Machine Learning-based Intrusion Detection Systems in Software-Defined Networks. It combines Mininet for network emulation and Ryu for SDN control, enabling advanced IDS research and deployment.

This repository contains the assignments of course "ITCS 8166: Computer Comm & Networks". This course has been taken in Spring 2021 semester, as part of my PhD degree at UNC Charlotte.

This course is designed to train the hands-on capabilities for solving networking problems. In this course, you will learn basic network management tools and use them to solve practical problems.

Real-time SDWAN traffic monitoring via ryu controller

Demonstrating an intelligent, adaptive load balancing system built on Software-Defined Networking (SDN) principles. The system uses a centralized Ryu controller to dynamically manage network traffic based on the real-time CPU load of backend application servers, complete with a live web-based monitoring dashboard.