Feature Engineering and Machine Learning Framework for DDoS Attack Detection in the Standardized Internet of Things

Distributed Denial of Service (DDoS) attacks are a major threat to cloud servers, and the rapid growth of Internet of Things (IoT) devices has further intensified this problem. Large-scale IoT-based DDoS attacks can overwhelm networks and disrupt essential services. To address this, we present a machine learning–driven, multi-layer detection framework that integrates IoT devices, Gateways, Software-defined networking (SDN) switches, and cloud servers. For experimentation, we deployed eight smart poles on our campus equipped with diverse sensors and gathered real-time data through both wired and wireless networks. Features relevant to different categories DDoS attacks were extracted and used to train machine learning models, achieving high detection accuracy in realistic IoT environments. Our results demonstrate that the proposed framework can effectively identify malicious traffic and leverage SDN controllers to block compromised devices in real time.