Adaptive Access Control in SHACS: Leveraging Markov Models and Topological Data Analysis for Enhanced Cloud Security
Keywords:
Cloud Healthcare, Security, Feature Optimisation, Topological Data Analysis (TDA), Adaptive Access Control, Markov Models, Detection Rate, Access Control Accuracy, False Positive RateAbstract
This paper proposes an advanced adaptive access control system for Smart Healthcare and Cloud Systems (SHACS) that combines Markov Models, Topological Data Analysis (TDA), and feature optimization. The system integrates Markov Chains' probabilistic modeling with the structural insights provided by TDA to dynamically assess user access requests. Markov Models predict future access patterns by examining historical data, while TDA analyzes the structural patterns of user interactions to identify anomalies and vulnerabilities in the cloud system. This fusion of methods enables the system to not only detect deviations from expected behavior but also predict and mitigate potential threats in real-time. In order to keep the system sensitive to changing security threats, the adaptive mechanism makes use of feedback loops to continuously update and improve access control policies based on real-time data. The approach improves security without sacrificing system efficiency by combining probabilistic predictions and topological insights to enable context-aware decision-making. Key security measures show notable gains once the integrated system was implemented, with anomaly detection hitting 97.63%, access control accuracy hitting 93.78%, and the false-positive rate dropping to just 2.45%. Furthermore, the system demonstrates enhanced efficiency and scalability, handling large numbers of access requests without affecting the cloud healthcare system's overall performance. In order to deliver reliable, secure, and flexible access control solutions for cloud-based healthcare environments—ensuring data protection and system resilience in dynamic settings—this study emphasises the need of merging cutting-edge techniques like Markov Models and TDA.
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