An Investigative Study of Adaptive Neuro-Fuzzy Networks for Robust Access Control

Abstract

This study explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in developing robust access control mechanisms to address the increasing complexity of cybersecurity threats. Traditional access control models often fail to adapt to dynamic environments, leading to vulnerabilities. We propose a framework that leverages the strengths of neural networks and fuzzy logic, enabling a more flexible and intelligent approach to access control. ANFIS utilizes its learning capabilities to analyze intricate, nonlinear relationships within user behavior data, facilitating real-time adjustments to access permissions based on contextual factors. The research methodology includes simulations and case studies to evaluate the framework's effectiveness. Results demonstrate that the ANFIS-based model significantly enhances both security and user experience, adapting swiftly to changes in user behavior and environmental conditions. Key findings reveal improved accuracy in identifying legitimate users while minimizing false acceptance rates. This work highlights the potential of ANFIS to create a more resilient access control system capable of evolving alongside emerging threats. Ultimately, the study contributes to the field of cyber security by presenting a novel approach that aligns adaptability with security needs, offering valuable insights for future access control strategies.