Search Results (3)

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments. Full article

Establishing service-driven IoT systems that are reliable, efficient, and stable requires building trusted IoT environments to reduce catastrophic and unforeseen damages. Hence, building trusted IoT environments is of great importance. However, we cannot assume that every node in wide-area network is aware of every other node, nor can we assume that all nodes are trustworthy and honest. As a result, prior to any collaboration, we need to develop a trust model that can evolve and establish trust relationships between nodes. Our proposed trust model uses subjective logic as a default artificial reasoning over uncertain propositions to collect recommendations from other nodes in the IoT environment. It also manages and maintains existing trust relationships established during direct communications. Furthermore, it resists dishonest nodes that provide inaccurate ratings for malicious reasons. Unlike existing trust models, our trust model is scalable as it leverages a Fog-based hierarchy architecture which allows IoT nodes to report/request the trust values of other nodes. We conducted extensive performance studies, and confirm the efficiency of our proposed trust model. The results show that at an early stage of the simulation time (i.e., within the first 2% of the number of transactions), our trust model accurately captures and anticipates the behavior of nodes. Results further demonstrate that our proposed trust model isolates untrustworthy behavior within the same FCD and prevents untrustworthy nodes from degrading trustworthy nodes’ reputations. Full article

In the Social Internet of Things (SIoT), trust refers to the decision-making process used by the trustor (Service Requesters (SRs) or Service Consumers (SCs)) to decide whether or not to entrust the trustee (Service Providers (SPs)) with specific services. Trust is the key factor in SIoT domain. The designing of a two-way, two-stage parameterized feedback-based, service-driven, attacks-resistant trust and reputation system for SIoT accompanied by a penalty mechanism for dishonest SPs and SRs is our main contribution that mitigates the trust-related issues occurring during service provisioning and service acquisition amongst various entities (SPs or SRs) and enhances trust amongst them. Our proposed methodology examines a SP’s local trust, global trust, and reputation by taking into account “Social Trust” and “Quality of Service (QoS)” factors”. Two—Stage Parameterized feedback” is incorporated in our proposed strategy to better manage “intention” and “ability” of SRs and provides early identification of suspicious SRs. This feature compels SRs to act honestly and rate the corresponding SPs in a more accurate way. Our recommended paradigm sorts SPs into three SP status lists (White List, Grey List, and Black List) based on reputation values where each list has a threshold with respect to the maximum service fee that can be charged. SPs in White List charge the most per service. SPs in other lists have a lower selection probability. Every feedback updates the SP’s trust and reputation value. Sorting SPs increases resistance against On Off Attack, Discriminatory Attack, Opportunistic Service Attack, and Selective Behavior Attacks. SPs must operate honestly and offer the complete scope of stated services since their reputation value relies on all their global trust values (Tglobal) for various services. Service requests may be accepted or denied by SPs. “Temporarily banned” SRs can only request unblocked services. SRs lose all privileges once on a “permanently banned” list. If local and global trust values differ by more than the threshold, the SR is banned. Our method also provides resistance against Bad Mouthing Attack, Ballot Stuffing Attack. Good Mouthing Attack/Self—Propagating Attack. Experiments indicate our trust and reputation management system recognizes and bans fraudulent SRs. “Dishonest SPs” are “blacklisted,” which affects their reputation, trust, and service charges. Full article