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.
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-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
This project helps network security professionals protect their digital infrastructure by identifying and responding to Distributed Denial-of-Service (DDoS) attacks. It takes real-time network traffic data as input and outputs a clear indication of ongoing DDoS attacks, enabling swift mitigation. This is designed for network security engineers and operations teams managing software-defined networks (SDN).
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