## 2 Related Work  
This section mainly reviews the widely studied trust model-based IDS and other types of IDS in UWSNs. Some related works of OR protocols in UWSNs are also introduced. Finally, the defects of the existing IDS protocol to protect OR security are introduced. The authors in [8] proposed an anti-attack trust model (ARTMM) based on multi-dimensional trust metrics to achieve accurate and efficient trust evaluation in UWSNs. ARTMM includes three trust measures, namely, link trust, data trust, and node trust. Direct trust and indirect trust are considered to improve the detection rate and reduce the false detection rate. A trust model for UWSNs based on cloud theory (TMC) is proposed in [7]. The purpose of TMC is to solve the uncertainty and ambiguity of trust and improve the accuracy of trust assessment. Moreover, TMC analyzes the packet loss layer by layer, which alleviates the influence of unreliable underwater environments and other factors on the accuracy of relevant evidence. A collaborative trust model based on SVM (STMS) is proposed in [9]. The trust prediction model was trained by a support vector machine (SVM) to evaluate the trust value accurately. The network is divided into a number of interconnected clusters, where cluster heads (CHs) and cluster members (CMs) collaborate to perform functions. The mutual supervision mechanism of two cluster heads is proposed to strengthen the security of the cluster heads. In order to ensure the security of UWSNs, ref. [10] proposed a security clustering protocol based on a weighted algorithm to calculate trust value. Only the direct trust value is considered in this protocol to avoid the extra communication energy consumption caused by transmitting the indirect trust value. In addition, the hierarchical trust management scheme is adopted, including the in-cluster node level, CH level, and sink node level. The authors in [11] analyzed the characteristics of node mobility and sound channel, and thus established an environment model that can reduce the influence of underwater environment on trust assessment. Instead of the traditional periodic update mechanism, the authors construct a trust update model to resist a dynamic attack of malicious nodes based on the environment model and reinforcement learning. In [12], the Isolation Forest algorithm is used to evaluate the reliability of sensor nodes based on the trust data set that is integrated with communication trust, data trust, energy trust, and environmental trust. The isolated forest algorithm is an unsupervised learning algorithm, which is well suited for unbalanced data sets with only a small number of negative samples. Therefore, this method is suitable for scenarios where there are a small number of malicious nodes in UWSNs and some new types of exceptions can be detected. First, SVM was used to conduct training modeling for the three types of trust evidence, and then DS evidence theory was used to fuse the three classification results in [13]. The hash underwater environment can easily misclassify normal nodes as malicious nodes. Thus, trust redemption is introduced to reduce the false detection rate by improving the trust value of nodes under an unreliable underwater acoustic channel and a weak link connection. Although many trust model-based IDS for UWSN security have been proposed, there are still some limitations. Based on the weighted algorithm in [8; 10], the determination of experience weight and trust threshold is subjective and will produce inaccurate results. The authors in [9] avoid the addition of trust measure and subjective weight, reduce the error of trust calculation, and effectively improve the detection accuracy of the defective nodes. However, when considering direct trust and indirect trust, it is difficult to distinguish credible indirect trust. The indirect trust information exchange between neighbor nodes results in an extra communication overhead.