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Title	Analyses Of Recent Advances On Machine Learning-Based Trust Management For Mobile Iot Applications
ID_Doc	9015
Authors	Souissi H.; Mahamat M.; Jaber G.; Lakhlef H.; Bouabdallah A.
Year	2022
Published	2022 30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022
DOI	http://dx.doi.org/10.23919/SoftCOM55329.2022.9911488
Abstract	The Internet of Things (IoT) has attracted attention by projecting the vision of a global infrastructure on a network of physical objects, enabling connectivity at any time and place to anything and not just for anyone. IoT has grown significantly in a wide variety of applications, including health, smart cities, smart vehicles, etc. In this context, an important part of these applications requires the mobility of terminals that move frequently and change locations, which makes the network vulnerable to multiple attacks. To ensure the security of these networks, several requirements must be taken into consideration, such as privacy, authentication, and trust among users. A certain amount of research has been made to solve the various security-related problems. Machine learning (ML) has an essential role in creating a smarter and more secure IoT, as it has shown remarkable results in different domains. Hence, our survey focuses on classifying and evaluating the existing trust-based security solutions using ML schemes in mobile IoT environments. © 2022 University of Split, FESB.
Author Keywords	IoT; Machine learning; Mobility; Security; Trust management

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