Abstract
DDoS attacks often happen in cloud servers and cause a devastating problem. However, an increasing number of Internet of Things (IoT) devices makes us not ignore the influence of large-scale DDoS attacks from IoT devices. In this paper, we propose a machine learning-based on a multi-layer IoT DDoS attack detection system, including IoT devices, IoT gateways, SDN switches, and cloud servers. Firstly, we build eight smart poles with various sensors on our campus and collect sensor data as our datasets through wireless networks or wired networks. Next, we extract the features based on DDoS attack types. The feature selection can result in high accuracy DDoS attack detection in the real IoT environment. The experimental results show that our multi-layer DDoS detection system can accurately detect DDoS attacks. And the SDN controller can block venomous devices effectively according to blacklists from the results of our IoT DDoS attacks detection system.