## 2 Related Work
### Trust Evaluation  
In [4], [PERSON] et al. introduced the Attack-Resistant Trust Model for underwater acoustic sensor networks (ARTMM). This model relies on a multidimensional trust metric to assess the trustworthiness of the network. The ARTMM specifically examines the challenges posed by unreliable underwater acoustic channels. It gathers trust-related data across various dimensions, encompassing link trust, data trust, and node trust, while also taking into account the influence of the underwater environment on trust evaluation. Nevertheless, it is worth noting that the trust evidence generation process in the ARTMM does not fully consider the potential impact of malicious attacks at each layer of the network. Furthermore, the application of fuzzy logic within the ARTMM may not adequately capture the nuances associated with uncertain trust relationships. In [11], [PERSON] et al. introduced a trust model tailored for underwater acoustic sensor networks, known as the trust model based on cloud theory (TMC). This model leverages the cloud theory, which is capable of accommodating factors such as randomness, fuzziness, and uncertainty inherent in trust evaluation. The TMC employs a systematic approach by conducting layer-by-layer packet loss analysis. This method helps mitigate the influence of unreliable acoustic channels and the dynamic nature of network topology on trust evaluation, ultimately enhancing the precision of detecting malicious nodes. However, it is worth mentioning that the TMC does not account for the impact of multiple malicious attacks on the trust evaluation process. In [12], [PERSON] et al. introduced an anomaly-resilient trust model centered around the isolated forest algorithm. By incorporating this algorithm, they enhanced the accuracy of detecting faulty nodes, while also introducing the concept of environmental trust, and used sub-sampling methods to reduce the probability of misclassification in trust evaluation. However, defining the metric of environmental trust as a linear model of the total noise PSD is incomplete. In [13], [PERSON] et al. introduced a trust model known as the Group-based Trust Management System (GTMS). This model involves the clustering of sensor nodes into groups for trust evaluation. Within the GTMS framework, trust evaluation occurs at multiple levels: node level, cluster head level, and base station level, achieved through the observation of communication behaviors among neighboring nodes. Node trust values are computed individually by each cluster member, with cluster heads responsible for gathering and maintaining the trust status of their respective cluster members, thus forming a global trust value for nodes within the group. However, it is important to note that the increased overhead associated with packet transmission and data storage tends to reduce the cluster head's operational lifespan compared to that of a typical node. Additionally, the potential delay arising from the transmission of node-to-node trust values was not addressed in the GTMS model. In [14], [PERSON] et al. introduced the SVM-based Collaborative Trust Model (STMS) designed for application in underwater acoustic sensor networks. This model incorporates a clustering network structure and introduces a dual cluster head mechanism. Notably, the slave cluster head's role is to oversee and monitor the master cluster head, enhancing the overall system's security. The STMS effectively tackles the challenge of limited evidence in sparsely deployed underwater environments. Nonetheless,it is worth noting that the STMS model does not take into account the potential influence of complex underwater environmental factors on the precision of trust prediction. In [15], [PERSON] et al. proposed a trust management mechanism for underwater acoustic sensor networks with redeemable SVM-DS fusion. The SDFTM takes into account the challenges posed by intricate underwater environments on node assessment.