DDoS detection for 6G Internet of Things: Spatial-temporal trust model and new architecture

With the rapid development of the sixth generation (6G) network and Internet of Things (IoT), it has become extremely challenging to efficiently detect and prevent the distributed denial of service (DDoS) attacks originating from IoT devices. In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness, which can be deployed in the access network of 6G IoT. Based on historical communication behaviors, this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices, thereby effectively distinguishing attack traffic. Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic. Compared with the benchmark methods, our method has advantages in terms of both accuracy and efficiency in identifying attack flows.