Enhanced Multilevel Fuzzy Inference System for Risk Adaptive Hybrid RFID Access Control System

Abstract

Risk-based access control systems are part of identity management systems used to accommodate environments with needs for dynamic access control decisions. The risk value is subjected to overestimation or underestimation since it is measured qualitatively, thus; causing uncertainty problems, which was apparent in a previously proposed hybrid risk adaptive (HRA) access control system. Conversely, Fuzzy Inference Systems can deal with the uncertainty of measures and control the outcomes more precisely; therefore, a multilevel fuzzy inference system (HRA-MFIS) was proposed to replace the risk assessment model in HRA. This paper continues to improve the previous model by introducing an enhanced multilevel fuzzy inference system (EHRA-MFIS), which utilizes user behaviour and time analysis to detect anomalous access behaviour. Moreover, it improves the hybrid adaptive risk calculation module by adding authentication, classification and the degree of user anomalous behaviour to the risk calculation algorithm. The results show that the proposed model has smoothed out the transition between the different risk levels and enhanced the system's overall security by considering the failed authorization attempts and failed authentication attempts, asset classification, and user behaviour when calculating the risk level.

Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes

“…The BDFIS uses a Mamdani-type FIS to specialize in binary decision outputs and optimize the output generation process. Mamdani-type FIS proved to be the most adaptable tool for binary decisions [12]. A novel fuzzy classification method related to a Mamdani-type fuzzy inference focuses on the induction of fuzzy rules from interval type-2 FIS (IT2FIS) [4].…”