DDoS and Flash Event Detection in Higher Bandwidth SDN-IoT using Multiagent Reinforcement Learning

Abstract

The emergence of 5G, IoT, Big Data, and related technologies have necessitated a shift to SDN architectural design and DRL algorithms for network task automation. Without prompt intelligent detection, the volumetric UDP flooding attack from zombies in an SDN-IoT network tends to consume network resources and mix with flash crowd events from legitimate hosts. This paper proposes a multiagent reinforcement learning framework in SDN-IoT to detect and mitigate DDoS attacks and route flash crowd events in the network effectively without compromising benign traffic. We simulated a 200 nodes topology with higher bandwidth and transmission rate in Mininet and implemented a multiagent deep deterministic policy gradient (MADDPG) algorithm for the framework. From the simulation results, the proposed approach outperforms Deep Deterministic Policy Gradient (DDPG) algorithm for the following network metrics: delay; jitter; packet loss; intrusion detection; and bandwidth utilization of network flows