ANFIS for risk estimation in risk-based access control model for smart homes

Abstract

The risk-based access control model is one of the dynamic models that use the security risk as a criterion to decide the access decision for each access request. This model permits or denies access requests dynamically based on the estimated risk value. The essential stage of implementing this model is the risk estimation process. This process is based on estimating the possibility of information leakage and the value of that information. Several researchers utilized different methods for risk estimation but most of these methods were based on qualitative measures, which cannot suit the access control context that needs numeric and precise risk values to decide either granting or denying access. Therefore, this paper presents a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for risk estimation in the risk-based access control model for the Internet of Things (IoT).