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Internet of Things (IoT) is an innovative era of interrelated devices to provide services to other devices or users. In Social Internet of Thing (SIoT), social networking aspect is used for building relationships between devices. For providing or utilizing services, devices need to trust each other in complex and heterogeneous environments. Separating benign and malicious devices in SIoT is a prime security objective. In literature, several works proposed trust computation models based on trust features. But these models fail to identify malicious devices. This paper focuses on detection of malicious devices. In this paper, basic fundamentals, properties, models and attacks of trust in SIoT are discussed. Up-to-date research distributions on trust management and trust attacks are reviewed and idea of Trust Management using Machine Learning Algorithm (TM-MLA) is proposed for identification of malicious devices.

International audience; L'Internet des Objets Social (SIoT) est un paradigme dans lequel l'Internet des Objets (IoT) est fusionné avec les réseaux sociaux. Dans ce type d'environnement, les participants sont en compétition afin d'offrir une variété de services attrayants. Néanmoins, certains d'entre eux ont recours à des comportements malveillants afin de propager des services de mauvaise qualité. Ils lancent ce qu'on appelle des attaques de confiance et brisent les fonctionnalités de base du système. Plusieurs travaux de la littérature ont traité ce problème et ont proposé différents modèles de confiance. Néanmoins, ces derniers proposent de classer les meilleurs noeuds du réseau SIoT. Ils ne permettent pas de détecter les noeuds malveillants. Pour remédier à ce problème, nous proposons un nouveau modèle de gestion de la confiance, capable de détecter et bloquer les noeuds malveillants afin d'obtenir un système fiable et résilient. ABSTRACT. The Internet of Things Social (SIoT) is a paradigm where the Internet of Things (IoT) is merged with social networks. In this type of environment, participants compete to offer a variety of attractive services. Nevertheless, some of them resort to malicious behavior in order to spread poor quality services. They commit so-called trust-related attacks and break the basic functionality of the system. Several works in the literature have addressed this problem and have proposed different trust-models. Nevertheless, they propose to classify the best nodes of the SIoT network. They do not detect different types of trust attacks or malicious nodes. To address this problem, we propose a new trust evaluation model able to detect and block malicious nodes in order to ensure a reliable and resilient system.

International audience; Social Internet of Things (SIoT) is a paradigm in which the Internet of Things (IoT) concept is fused with Social Networks for allowing both people and objects to interact in order to offer a variety of attractive services and applications. However, with this emerging paradigm, people feel wary and cautious. They worry about revealing their data and violating their privacy. 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 provide qualified services and improved security. Several works in the literature have dealed with this problem and have proposed different trust-models. Nevertheless, proposed models aim to rank the best nodes in the SIoT network. This does not allow to detect different types of attack or malicious nodes. Hence, we overcome these issues through proposing a new trust evaluation model, able to detect malicious nodes, block and isolate them, in order to obtain a reliable and resilient system. For this, we propose new features to describe and quantify the different behaviors that operate in such system. We formalized and implemented a new function learned and built based on supervised learning, to analyze different features and distinguish malicious behavior from benign ones. Experimentation made on a real data set prove the resilience and the performance of our trust model.