hainamt/anomaly-dectection-ml-sdn
A prototype for an architecture that uses Machine Learning to detect abnormalities in the SDN network packet flow, built by Ryu SDN and mininet
This architecture prototype helps network operations teams detect unusual activity in their Software-Defined Networking (SDN) environments. It takes raw network traffic data (PCAP files) and uses machine learning to identify anomalous patterns. The output is a detection of suspicious source IP addresses, allowing for automated responses within the network.
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Use this if you are a network engineer or security analyst managing an SDN and need an automated way to spot abnormal network behavior that could indicate a security threat or operational issue.
Not ideal if you are looking for a production-ready system, as this is a prototype and would require significant optimization for real-world deployment.
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Python
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Last pushed
Jun 25, 2023
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