Machine Learning Techniques for the Detection of Distributed Denial of Service Attacks in the SDN
Keywords:
SDN, attacks, DDoS, Decision Tree.Abstract
A network architecture known as a "software-defined network" (SDN) is used to digitally construct and design hardware components. The network connection settings can be changed dynamically. Because the link is fixed in the conventional network, dynamic change is not feasible. SDN is a wonderful strategy, but DDoS assaults can still happen. The DDoS assault poses a threat to the internet. The machine learning algorithm can be used to stop DDoS attacks. The DDoS assault is when several systems work together to simultaneously target a certain host. In SDN, the infrastructure layer's devices are managed by software from the control layer, which sits in the middle of the application and infrastructure layers. We provide a machine learning method called Decision Tree in this research to identify malicious communications. Our test results demonstrate that the Decision Tree determines whether or not the assault is safe.
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