A hybrid trust computing approach for IoT using social similarity and machine learning.

ABSTRACT: Every year, millions of new devices are added to the Internet of things, which has both great benefits and serious security risks for user data privacy. It is the device owners' responsibility to ensure that the ownership settings of Internet of things devices are maintained, allowing them to communicate with other user devices autonomously. The ultimate goal of the future Internet of Things is for it to be able to make decisions on its own, without the need for human intervention. Therefore, trust computing and prediction have become more vital in the processing and handling of data as well as in the delivery of services. In this paper, we compute trust in social IoT scenarios using a hybrid approach that combines a distributed computation technique and a global machine learning approach.

SUBMITTER: Ali-Eldin AMT 

PROVIDER: S-EPMC9333244 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature