A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) have been widely deployed in many areas, such as marine ranching, naval applications, and marine disaster warning systems. The security of UASNs, particularly insider threats, is of growing concern. Internal attacks carried out via compromised normal nodes are more damaging and stealthy than external attacks, such as signal stealing, data decryption, and identity forgery. As a security mechanism for internal threat detection based on interaction data, trust models have proven to enhance the security of UASNs. However, traditional trust models lack sufficient scalability when faced with movable underwater devices, heterogeneous network environments, and variable attack patterns. Therefore, in this paper, a novel trust model based on federated deep reinforcement learning is proposed for UASNs. First, the evidence acquisition mechanism, including communication, energy, and data evidence, is improved based on existing ones to better accommodate the topological dynamics of UASNs. Second, acquired trust evidence is fed into the corresponding deep reinforcement learning-based local trust model to accomplish trust prediction and model training. Finally, a federated learning-based update method periodically aggregates and updates the parameters of the local models. The experimental results prove that the proposed scheme exhibits satisfactory performance in terms of improving trust prediction accuracy and energy efficiency.