# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 3 Related Work  
Unreliable recommendations have been categorised into two types: dishonest recommendations and wrong recommendations [19]. Dishonest recommendations are a form of subjective unreliability where adversaries intentionally offer misleading advice. Wrong recommendations, on the other hand, represent objective unreliability that arises from unavoidable errors in transmission or computation. Wrong recommendations are expected to be prevalent in unpredictable and complex environments, such as the application of IoUT. Therefore, existing techniques for filtering dishonest recommendations should account for these challenges. Addressing the challenge of mitigating the effects of dishonest recommendations in reputation- and recommendation-based trust systems remains challenging. In efforts to protect the integrity of reputation and recommendation systems from recommendation attacks, certain models have been developed. For instance, the Collaborative Reputation Mechanism (CORE) [20] filters out negative feedback under the assumption that ballot-stuffing attacks are non-existent, allowing only positive reputation data to spread and thus preventing bad-mouthing attacks. In contrast, the CONFIDANT [21] model focuses on circulating only negative reviews about other nodes to thwart ballot stuffing. Both approaches, however, struggle with efficiency in detecting unpredictable behaviours in complex networks as they permanently block either positive or negative feedback. [PERSON] et al. outlines two primary methods prevalent in the literature for evaluating trust in networks: Deviation Tests and Evaluation using Trust Factors [15]. The deviation test involves comparing individual opinions about an entity against a predefined threshold to identify and isolate disproportionately negative or positive recommendations. This method is exemplified in the Distributed Reputation-based Beacon Trust System (DRBTS), a distributed security protocol that enables beacon nodes to monitor each other and relay trust information [22]. The network is modelled as an undirected graph, where deviation tests are used to ensure consistency between first-hand (personal opinion) and second-hand (recommendation) information, thereby preventing the spread of false data. Similarly, the E-Hermes protocol, introduced by [PERSON] et al., integrates first-hand trust data, which nodes compute independently, with second-hand trust data obtained from other nodes' recommendations [23]. E-Hermes is designed to increase resilience against malicious nodes and recommenders by implementing a recommender's test that validates recommendations against the first-hand trust values computed by the inquiring node. This process ensures that only recommendations closely aligned with independently assessed values are accepted. However, these techniques, especially in dynamic and complex environments like the IoUT, face limitations due to their heavy reliance on personal experiences, which can be problematic given the sparse and mobile nature of these networks. The decay of trust information and the infrequent opportunities for validation in such a rapidly changing network make it difficult to maintain accurate trust assessments. Additionally, the increased overhead and vulnerability to strategic manipulation by nodes adjusting their behaviour to evade detection further challenge the effectiveness of these methods in IoUT environments. Others approach this issue by introducing so-called Trust Factors to help in judgment on the recommendations. For instance, in [24], a trust-based recommendation model known as TBRS for vehicular cyber-physical system networks is introduced. This model evaluates recommendations using trust factors such as contact intimacy, delivery reliability, and position intimacy. These factors are weighted according to their Grey relational degrees to enhance judgment accuracy. Additionally, the K-Nearest Neighbour (KNN) algorithm is employed as a filtering mechanism to mitigate the impact of selfish or malicious nodes.