project and DDoS-attack-detection-and-mitigation-using-deep-neural-network-in-SDN-environment
Both tools use machine learning on SDN networks to detect DDoS attacks, but they are **competitors** — one employs traditional SVM classification while the other uses deep neural networks, representing different algorithmic approaches to solve the same detection problem.
About project
GAR-Project/project
DDoS attacks detection by using SVM on SDN networks.
Integrates Telegraf for metrics collection, Mininet for SDN network emulation, and InfluxDB/Grafana for time-series data storage and visualization. The system trains an SVM classifier on network traffic features to distinguish between normal and DDoS activity, with a distributed architecture separating the Ryu SDN controller from the emulated network environment. Deployment is streamlined through Vagrant-based virtualization, ensuring reproducible multi-VM setups with hping3 for traffic generation and testing.
About DDoS-attack-detection-and-mitigation-using-deep-neural-network-in-SDN-environment
vanlalruata/DDoS-attack-detection-and-mitigation-using-deep-neural-network-in-SDN-environment
Computers & Security
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