An Adaptive and Scalable DDoS Prevention Framework for Software Defined Networks

Abstract

Central governance and flexible network administration are made possible by Software Defined Networking (SDN); still, this architectural benefit also makes the control plane vulnerable to Distributed Denial of Service (DDoS) attacks. An extreme number of flow requests and packet-in events can significantly reduce controller effectiveness and interfere with network functions in the context of such attacks. In this work, we change and estimate an adaptive DDoS prevention framework based on knowledge gained from SDN emulation tests. Relatively than relying on predetermined mitigation thresholds, the framework dynamically adjusts mitigation strategies based on the attack's severity and the controller's present load. The proposed approach reduces unnecessary interactions in the control plane while maintaining service quality for authorized traffic by incorporating controller-aware decision-making. The adaptive outline lessens controller CPU utilization, speeds up mitigation response times, lowers end-to-end latency, and keeps higher throughput when compared to static mitigation procedures, according to experimental evaluations carried out in a precise SDN emulation environment