BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

Regateiro, Diogo Domingues; Pereira, Óscar Mortágua; Aguiar, Rui L.

doi:10.18293/seke2019-039

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…A binary decision of whether to grant or deny access using FIS was proposed in [22]. Since the access decision is binary, Mamdani FIS was used because it is the most adaptable tool for binary decisions.…”

Section: Fuzzy Logic Access Control Methodsmentioning

confidence: 99%

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

2022

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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.

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Section: Fuzzy Logic Access Control Methodsmentioning

confidence: 99%

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

2022

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“…The system was developed using the adaptive neuro-fuzzy inference system (ANFIS) and decision trees to obtain the numerical parameters of the MFs and the linguistic-based rules, respectively. A binary decision access control model based on FIS (BDFIS) that may make binary access control decisions was also proposed [11]. The BDFIS uses a Mamdani-type FIS to specialize in binary decision outputs and optimize the output generation process.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes

Tašić,

Nagy-Perjési,

Takács

2024

Mathematics

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In this paper, we present a multilevel fuzzy inference model for predicting the risk of type 2 diabetes. We have designed a system for predicting this risk by taking into account various factors such as physical, behavioral, and environmental parameters related to the investigated patient and thus facilitate experts to diagnose the risk of diabetes. The important risk parameters of type 2 diabetes are identified based on the literature and the recommendations of experts. The parameters are scaled and fuzzified on their own universe and, based on the experts’ recommendation, fuzzy inference subsystems are created with 3–4 related risk parameters to calculate the risk level. These sub-systems are then arranged into Mamdani-type inference systems so that the system calculates an aggregated risk level. The overview of the large number of diverse types of risk factors, which may be difficult for specialists and doctors, is facilitated by the proposed system.

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