Trust Model for Underwater Wireless Sensor Networks Based on Variational Autoencoders

In underwater wireless sensor networks(UWSNs), the complex underwater acoustic communication environment and the limited resources of nodes make malicious node attacks more covert and threatening. Therefore, researching effective malicious node detection methods is crucial for maintaining network stability and data security. This paper proposed a trust model for UWSNs based on variational autoencoders(VAEs), which evaluated node behavior credibility to identify malicious nodes. First, the model aggregated the behavioral feature data from the underwater node transmission process, extracting various indicators such as node location, packet delivery ratio, and delay, thereby forming a trust dataset. The dataset was then encoded and trained, and variational inference was employed to map the data to a latent space and obtain the probability distribution of this space. Finally, based on the probability distribution, the model decoded and reconstructed the data to derive node behavior credibility, thus completing the trust evaluation of nodes. Comparative experimental results show that compared to methods such as the intrusion detection-based trust management system, the proposed model improves trust evaluation accuracy by at least 10.5% and demonstrates significant advantages in operational stability.