DDOS (Distributed Denial of Service) Attack Detection and Mitigation Using Statistical and Machine Learning Methods in SDN (Software-Defined Networking)
Keywords:
Distributed Denial of Service, Software-Defined Networking, quality of service, User Datagram Protocol, Internet Control Message Protocol, Transmission Control Protocol YNchronization , principal component analysis, Support Vector Machines, K-Nearest Neighbors , Random Forest , Neural NetworksAbstract
This study focuses on addressing the growing threat of Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) environments. DDoS(Distributed Denial of Service attacks can cause significant disruption to network services by overwhelming target systems with a flood of malicious traffic. To combat this, we propose a novel approach that combines statistical and machine learning methods for the detection and mitigation of DDoS(Distributed Denial of Service attacks in SDN .To implement the detection and mitigation system, we design and deploy a comprehensive framework within the SDN infrastructure [1].
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