An IoT Crossdomain Access Decision-Making Method Based on Federated Learning

Abstract

Crossdomain collaboration allows smart devices work together in different Internet of Things (IoT) domains. Trusted third party-based solutions require to fully understand the access information of the collaboration participants to implement crossdomain access control, which brings privacy risk. In this paper, we propose a federated learning-based crossdomain access decision-making method (FCAD), which builds a crossdomain access decision-making model without sharing privacy information of collaboration participants. Crossdomain access logs are extracted to construct a training dataset. Data enhancement method is used to address the uneven distribution of the dataset. Federated learning and gradient aggregation methods are used to prevent privacy leaks. The experiments on the public dataset show that FCAD obtains a prediction accuracy of 83.6% in the existing crossdomain access system.