project and DDoS-Detection-SDN
About project
GAR-Project/project
DDoS attacks detection by using SVM on SDN networks.
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.
About DDoS-Detection-SDN
aliasar1/DDoS-Detection-SDN
This repository contains the implementation of a DDOS attack detection system using a Software-Defined Networking (SDN) network.
This system helps network security engineers detect Distributed Denial of Service (DDoS) attacks in real-time. It takes raw network traffic data, processes it, and then uses various machine learning models to classify the traffic as either normal or a DDoS attack. Network administrators and security teams would use this to protect their network infrastructure from malicious attacks.
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