Smart Home IoT Network Risk Assessment Using Bayesian Networks
Miguel Flores, Diego Heredia, Roberto Andrade, Mariam Ibrahim
Abstract
A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur in the IoT network. The parameters of the Bayesian network are estimated with the maximum likelihood method applied to a data set obtained from the simulation of attacks, in five simulation scenarios. For the risk assessment, inferences in the Bayesian network and the impact of the attacks are considered, focusing on DoS attacks, MitM attacks and both at the same time to the devices that allow the automation of the smart home and that are generally the ones that individually have lower levels of security.