AI-Driven Distributed Adaptive Security in 6G Networks for Digital Healthcare

The rise of 6G networks and the proliferation of the Internet of Things (IoT) are revolutionizing digital healthcare with applications such as telemedicine and remote patient monitoring. The centralized security architecture of 5G falls short in meeting the stringent requirements of these applications. Centralized systems are particularly vulnerable to single points of failure, resulting in increased latency, limited scalability, and an inability to manage the inherently distributed nature of 6G systems. These challenges exacerbate security risks, amplify privacy concerns, and hinder efficient resource management in dynamic healthcare environments. To address these limitations, this research proposes a distributed adaptive security architecture tailored for 6G networks. By leveraging blockchain for decentralized authentication and machine learning for real-time threat detection, the proposed solution enhances scalability, trust, and resource efficiency. The architecture is underpinned by an edge-to-cloud continuum, enabling secure and resilient operation in resource-constrained settings. This work evaluates the effectiveness of the architecture in addressing security and efficiency challenges in healthcare, offering a robust framework for secure 6G-enabled healthcare applications.