In-network DDoS detection and mitigation using INT data for IoT ecosystem

Abstract

Due to the limited capabilities and diversity of Internet of Things (IoT) devices, it is challenging to implement robust and unified security standards for these devices. Additionally, the fact that vulnerable IoT devices are beyond the network’s control makes them susceptible to being compromised and used as bots or part of botnets, leading to a surge in attacks involving these devices in recent times. We proposed a real-time IoT anomaly detection and mitigation solution at the programmable data plane in a Software-Defined Networking (SDN) environment using Inband Network telemetry (INT) data to address this issue. As far as we know, it is the first experiment in which INT data is used to detect IoT attacks in the programmable data plane. Based on our performance evaluation, the detection delay of our proposed approach is much lower than the results of previous Distributed Denial-of-Service (DDoS) research, and the detection accuracy is similarly high.