A Trust Management Method Based on Ensemble Learning for Ocean-Oriented Cloud–Edge Collaborative Networks

Currently, the Internet of Underwater Things (IoUT) plays an important role in ocean exploration, monitoring, and protection. However, it faces many security threats due to resource constraints, such as denial-of-service attacks. To overcome these challenges, a novel underwater network architecture that incorporates cloud computing and edge computing technologies, namely, the ocean-oriented cloud edge collaboration networks (O-OCECNs), is designed. O-OCECNs, while enhancing computational capabilities, still faces network threats, such as energy depletion attacks, data pollution attacks, etc. On this basis, a trust management mechanism called ETrust is proposed in this article, which uses ensemble learning to ensure network security. In the ETrust mechanism, nodes collect trust evidences by monitoring the behavior and communication results of other nodes and then deliver the evidences to the cluster head node. The cluster head node uploads the collected evidences to the edge server in its region to complete the trust value computation. Then, the edge server uploads the trust values to the cloud server, which utilizes the built-in learning to complete the trust evaluation. Finally, the cloud server outputs the trust evaluation value to the cluster head node and it adjusts the network topology. The experimental result demonstrates that the proposed scheme can detect malicious nodes with a trust evaluation accuracy of up to 98.55%. It also performs well in capturing selective forwarding attacks occurring in the network. Furthermore, the mechanism enhances network lifetime more effectively compared to existing schemes. Experimental result shows that our scheme maintains the average residual energy of devices at approximately 63%, compared to 50% with other methods.