Enhancing IoT security and healthcare data protection in the metaverse: A Dynamic Adaptive Security Mechanism

In the rapidly evolving landscape of the Metaverse, the synergistic integration of the Internet of Things (IoT) and Digital Twins (DT) represents a revolutionary paradigm shift, seamlessly bridging the real and virtual worlds. While this innovative convergence offers unprecedented potential, it also exposes a broader spectrum of security vulnerabilities that challenge conventional approaches. This research aims to fortify the multifaceted ecosystem of the Metaverse, with a particular emphasis on securing IoT healthcare data. Ensuring the protection of health information within the extensive network of interconnected devices in the Metaverse is paramount. Addressing this critical need, we introduce the Dynamic Adaptive Security Mechanism (DASM), an advanced Artificial Intelligence (AI)-driven framework meticulously crafted to enhance security adaptively. DASM operates as a comprehensive and real-time defensive layer, continuously recalibrating its strategies to reinforce the security matrix for both IoT and Digital Twins. This study provides a detailed examination of the foundational architecture of DASM and its AI-driven adaptive processes. We elucidate its pivotal role in strengthening the security framework within the complex terrain of the Metaverse. Through rigorous testing and validation using the IoT healthcare security dataset, the Random Forest model emerges as the top performer, achieving near-perfect metrics, including a Matthews Correlation Coefficient (MCC) of 0.9989 and superior Balanced Accuracy, while also offering reduced training and inference times compared to the LSTM model. Although the LSTM model demonstrates strong accuracy, the ensemble approach of Random Forest balances computational efficiency and performance. The DASM framework sets a new benchmark in IoT security, offering a scalable and effective solution with significant implications for the future of Metaverse applications.