Adaptive Fuzzy Logic Risk- Based Access Control Model for Smart Contract Execution on Block Chain Systems

Abstract

Smart contracts contribute to the automation and efficiency of various processes, reducing the need for intermediaries in order to execute agreements on the blockchain platforms. Classical traditional access control models, Discretionary Access Control (DAC), Mandatory Access Control (MAC), and Role-Based Access Control (RBAC) represent conventional access control paradigms which have played a fundamental role in the management of resource access in many organizations and systems. These approaches employ predefined policies and conventions to govern and enforce access permissions. The access models of smart contracts deployed in Blockchain systems exhibit limited adaptability to dynamic changes in the system environment. Conventional crypto, the main access control mechanism encounters a challenge in effectively mitigating the security risks related to identity management authentication across the processes of consensus, initiation, and execution of smart contracts, specifically within Blockchain systems. The shortcoming of classical access control models, which were established in previous times lie in their lack of adaptability and responsiveness in detecting abnormal and malevolent behaviors through the process of observing and tracking user actions during the entirety of their access session. The situation at hand necessitates the implementation of adaptive access control models.  The aim of this paper is to propose the creation of a fuzzy logic risk-based access control model that is both dynamic and adaptive. The study will approach the proposed model creation, testing and evaluation by adopting a Mixed-Method research design which includes Experimental research design. Action research design will be used to test and evaluate the model anchored on within the PiECE framework. A fuzzy logic inference principle with expert judgment technique will be employed to evaluate the model through an evaluation metric criterion using regression model analysis. To handle uncertain data ranges, encompassing categories such as severe, high, moderate, and low user risk estimating strategy based on fuzzy logic shall be employed. Data collection methods will utilize the Ai data mining technique route thereafter involve cleaning, pre-processing and annotation of the sample size data sets. The cleaned data will be split into training, testing and validating data sets which then empirically, the MATLab toolkit will be used in the development and testing phase of the proposed architecture for execution stages of smart contracts in blockchain platform. Ethical concerns shall be highlighted based on the pilot model’s efficacy. The attributes of the adaptive fuzzy logic access control model will be utilized in future to design intelligent contracts that dynamically adjust the capabilities of users’ based on their behaviors throughout access sessions to enhance further smart contracts’ inherent secured nature.