ash0545/sdn-ml-ids
SDN Topology Emulation and Development of Dataset for ML-Based Intrusion Detection through the Ryu SDN Framework, Mininet and VirtualBox VMs
This project helps network security analysts and researchers by providing a method to generate realistic network traffic data for Software-Defined Networks (SDNs). It takes various simulated network attacks (like DDoS, probe attacks) and normal traffic flows, and outputs a structured dataset containing network flow statistics. This data is then used to train and compare machine learning models for intrusion detection.
Use this if you need to create a custom, labeled dataset of normal and attack traffic for a Software-Defined Network to develop or test machine learning-based intrusion detection systems.
Not ideal if you require an off-the-shelf, plug-and-play intrusion detection system or if your focus is on traditional network architectures rather than SDNs.
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Last pushed
Nov 23, 2025
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