# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 1 Introduction  
Trust and reputation models have long been integral to security disciplines, serving as essential tools for evaluating the trustworthiness of entities within a network [1]. These models operate by aggregating and analyzing various pieces of evidence to derive trust values, which are critical for detecting misbehaviour. The trust evaluation process involves systematically collecting data on entity interactions, formulating a trust score based on these data, and continuously updating this score as new information becomes available [2]. In the context of security systems, recommendations play a pivotal role in shaping personal trust opinions. These recommendations often utilize the concept of indirect trust, which entails forming trust towards an entity based on the opinions of others [3]. Indirect trust enhances the robustness of trust assessments by integrating multiple viewpoints; however, it also introduces significant vulnerabilities. For instance, dishonest recommendation tactics can severely disrupt the processes of trust establishment, propagation, andmaintenance. This disruption poses a threat to the integrity of the entire communication system [4]. A specific example of such disruption is the wrongful promotion of a benign node as malicious, aimed at isolating the node from network collaborations, thereby hindering its functionality and undermining the network's overall security posture. Conversely, a malicious node could be promoted as benign through the manipulation of the trust model, achieved by propagating dishonest recommendations that interfere with the trust scores of these entities. The Internet of Underwater Things (IoUT) is often characterized by its open and largely unmonitored nature [5]. The lack of a comprehensive security infrastructure in IoUT, coupled with inherent network sparsity, makes it a prime target for attackers who wish to exploit these vulnerabilities. These challenges are exacerbated by the underwater environmental conditions, where signal propagation and node mobility further complicate trust assessments [6]. Effective trust modelling in IoUT must address these limitations, as discrepancies in collected data can significantly impact trust decisions [7]. The challenge is particularly pronounced in scenarios with limited personal opinions, making it difficult to accurately establish trust. This issue becomes critical in environments where the lack of comprehensive and reliable trust data can lead to erroneous trust decisions, ultimately compromising the network's security and functionality. This paper proposes a novel recommendation evaluation process aimed at detecting dishonest recommendations within the context of trust establishment among IoUT nodes. The primary contributions of this paper are as follows. Firstly, we explore existing mechanisms for resisting and detecting dishonest recommendations, assessing their readiness and applicability within the unique context of IoUT. This involves a detailed analysis of current methodologies and their potential limitations when applied to underwater communication networks. Secondly, we develop a detection mechanism for dishonest recommendations that incorporates a combination of advanced filtering methods. This mechanism is designed to enhance the accuracy and reliability of detecting anomalies in recommendation data, thereby improving the overall trustworthiness of the network. Thirdly, we introduce a belief function that operates as a weighting method for recommendations. This belief function is based on several factors derived from the social aspects of validation, such as freshness, similarity, trustworthiness, and the decay of trust over time. Lastly, we conduct a comprehensive evaluation of the proposed method, benchmarking it against the current state-of-the-art approaches. This evaluation includes conceptual analysis and simulations to demonstrate the efficacy and robustness of the proposed method. The remainder of this paper is structured as follows.