Anotace:
Although the Internet of Things (IoT) is a rapidly developing technology, it also brings a number of security challenges, such asIoT attacks. Currently, research on IoT anomaly detection in Software-Defined Networking (SDN) relies only on the control plane.In this study, we aim to detect IoT anomalies by covering the advantages of the control and data plane. First, we collected real-timenetwork telemetry data from the data plane based on the capabilities of the P4. Then, using this telemetry data, we built differentanomaly detection models and compared their performance. Among them, the one-Dimensional Convolutional Neural Network (1DCNN) model classified our data best and showed the highest performance, so we proposed this model for IoT anomaly detection on thecontrol plane. To our knowledge, our approach is the first solution that integrates the control plane and data plane for IoT anomalydetection. Finally, when evaluating the performance of our proposed 1D CNN model, the accuracy, F1 score, and Matthews correlationcoefficient (MCC) are the same or better than existing studies