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Gestion de la confiance et des recommandations dans le SIoT

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Publications

  • 2024
    Hamdi Ouechtati, Nadia Ben Azzouna

    Towards an Adaptive Trust Management Model Based on ANFIS in the SIoT

    SECRYPT 2024: 710-715, 2024

    Résumé

    The integration of social networking concepts into the IoT environment has led to the Social Internet of Things
    (SIoT) paradigm which enables connected devices and people to facilitate information sharing, interact, and
    enable a variety of attractive applications. However, with this emerging paradigm, people feel cautious and
    wary. They worry about violating their privacy and revealing their data. Without trustworthy mechanisms to
    guarantee the reliability of user’s communications and interactions, the SIoT will not reach enough popularity
    to be considered as a cutting-edge technology. Accordingly, trust management becomes a major challenge
    to improve security and provide qualified services. Therefore, we overcome these issues through proposing
    an adaptive trust management model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to
    estimate the trust level of objects in the Social Internet of Things. We formalized and implemented a new trust
    management model built ANFIS, to analyze different trust parameters, estimate the trust level of objects and
    distinguish malicious behavior from benign behaviors. Experimentation made on a real data set proves the
    performance and the resilience of our trust management model.

  • Hamdi Ouechtati, Nadia Ben Azzouna, Lamjed Ben Said

    A fuzzy logic-based model for filtering dishonest recommendations in the Social Internet of Things

    Journal of Ambient Intelligence and Humanized Computing, 14(5), 6181-6200., 2023

    Résumé

    In the recent year, Internet of Things (IoT) has been adopted in several real-world applications such as smart transportation, smart city, retail, agriculture, smart factory, etc. to make human life more reliable. The integration of social networking concepts into the IoT led to the rise of a new paradigm: the Social Internet of Things (SIoT). In the SIoT environment, the objects are capable of establishing in an autonomous way many social relationships anywhere and anytime with other trusted objects. However, in such environment, objects may provide dishonest recommendations due to malicious reasons such as bad mouthing, ballot stuffing, random opinion, etc. In order to cater these challenges, we propose a new fuzzy logic-based model to filter dishonest recommendations and estimate their trust level based on (1) their values and sending time and the place coordinates and (2) the social relationship parameters of the recommenders. Results prove that our proposed approach is able to detect 100% of the fake Sybil attack and achieves 100% of Recognition Proportion, Sensitivity, Specificity, Accuracy and F1 score and gets 0% of False Negative and False Positive Proportions in presence of up to 90% dishonest recommendations.