Performance analysis of the dynamic trust model algorithm using the fuzzy inference system for access control. (June 2021) - Abirami, G; Venkataraman, Revathi
Highlights: The Novel Dynamic Trust Model Algorithm is used to address the dynamicity problems of trust to access the resources. Dynamic trust value is calculated based on four parameters such as Performance, Direct observation, Feedback and Expected trust. The Mathematical trust calculations for business data have been framed. Using Mamdani fuzzy inference rules the trust values have been classified. The performance measuring metrics such as precision, Mean Absolute Error (MAE), recall, F-Score and ROC have been found in Dynamic Trust Model Algorithm. Abstract: Accessing company resources such as personal data, financial data, and company networks in the dynamic business environment is a vital task. This paper proposes a Dynamic Trust Model Algorithm (DTMA) using fuzzy inference rules for access control. The novelty is finding the unsteady behaviour of an employee in a varying period using trust mathematical computation. Based on four parameters such as Performance (P), Direct Observation (DO), Expected Trust (ET) and Feedback (F), the trust value is calculated. To manage the deliberate altering behaviour of the hostile employees, a dynamic Trust Value (TV) has been calculated and restrict their harmful actions. Also, the performance and accuracy of the DTMA have been assessed and compared to other models such as DLATrust, DyTrust and SecTrust. The proposed DTMA gives better results in terms of accuracy, precision, recall, F-Score and Receiver Operating Characteristics (ROC). Graphical abstract: Image, graphical abstract