Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Abstract

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.

FRAMH: A Federated Learning Risk-Based Authorization Middleware for Healthcare

“…Various researchers address issues related to changing situations in healthcare environments. Recently, Atlam et al [15] presented a novel Neuro-Fuzzy System, which is a combination of an artificial neural network and a fuzzy logic system, to estimate risk in a risk-based access control model and applied it to a children's hospital. Risk is estimated according to the action and the sensitivity of the data involved, while the patient health status condition is not considered in the risk factor evaluations.…”

An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

“…They developed another classification-based method for risk access control, and this strategy involves classifying risk with authorization and integrating an access control matrix with role-based access control to award permissions to the roles that provide the least amount of danger. Atlam et al, [36] recently proposed using a neuro-fuzzy system model to estimate the value of the security risk that is associated with each access request for various Internet of Things applications. The results of the trials demonstrated that the proposed method provides access decisions that are dynamic and aware of the context in which they are being made based on real-time information.…”