# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 7 Experiments and Performance Evaluation
### Effectiveness Evaluation of the Proposed Model  
We conducted 20 trials with different network topologies that randomly distributed underwater nodes to evaluate the model's performance under diverse and random establishments. This approach is consistent with similar studies such as [45] to average the results based on multiple runs. In each trial, we examined the effectiveness of the proposed recommendation evaluation model. We varied the percentage of malicious recommendations from 0% to 50% in each trial, with 0% indicating no attacks. We then examined the trust establishment process under both bad-mouthing and ballot-stuffing recommendation attacks, where a proportion of neighbouring nodes propagate dishonest recommendations. In the case of a ballot-stuffing attack, we introduced malicious nodes into the network that behaved badly. In these experiments, malicious nodes refused to cooperate after consuming their energy to mimic selfish attacks. We set dishonest recommendations to artificially boost the trustworthiness of malicious nodes by increasing their trust scores. Within each test, we set the initial trust evaluation process to begin at 40 s of the simulation and periodically updated it every 60 s [33]. We examine the impact of different attack percentages on the belief score (\(B_{ij\gets k}\)) obtained in the proposed model among benign and malicious nodes. The belief scores of the nodes were measured, and the results are presented in Figure 8 and Figure 9, which represent the frequency distribution of obtained belief scores for both benign and malicious nodes across all experiments under ballot stuffing and bad-mouthing, respectively. Figure 8 shows how frequently each belief value appears among the nodes, effectively illustrating the impact of varying attack intensities on the belief assignments to benign and malicious nodes. At 0% attack, only benign nodes are present. The histogram for benign nodes (shown in blue) is relatively narrow, indicating a tight distribution around the mean belief. The belief score obtained here reflects the weight assigned by the model to each recommender among the received recommendations, with the variation reflecting the number of recommendations received by nodes and the associated weight assigned to them. This suggests that overall, benign nodes have consistent belief scores under normal conditions, with slight variations depending on the number of recommendations and the factors considered in the computation of the belief model, such as similarity, trustworthiness, and freshness. As the attack percentage increases from 10% to 50%, the graph displays both benign (blue) and malicious (red) nodes. There is a noticeable shift in the belief assigned to malicious nodes compared to benign ones, with malicious nodes typically receiving lower belief values. This indicates that the proposed model is capturing their altered and potentially disruptive behaviour. The distinction between the blue and red distributions diminishes as the attack percentage rises, highlighting the increasing influence of malicious actions. In the worst-case scenario, with 50% of nodes being malicious, the proposed model reduces the belief scores for the malicious nodes but still faces difficulty in fully separating the distributions compared to less intensive attacks. This challenge arises because when half of the network consists of malicious nodes, their collective influence becomes significant enough to obscure the clear distinction between benign and malicious behaviour. Similarly, Figure 9 illustrates the belief scores acquired during bad-mouthing attacks. The proposed model effectively recognises malicious nodes by lowering their belief scores. However, its performance declines in scenarios where half of the recommendations (50%) are malicious, though it still exhibits slight resistance favouring benign nodes.