Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, and native AI pose threats such as eavesdropping, supply chain vulnerabilities, model poisoning, and adversarial attacks. The increased exposure of sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3GPP, ETSI, OEMs and researchers to embed security and resilience as core components of 6G. While research is advancing, establishing a comprehensive security framework remains a significant challenge. To address these evolving threats, our research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats. By refining model accuracy and ensuring alignment through recursive feature elimination and consistent cross-validation, our approach strengthens the overall security posture of the IoT–6G ecosystem, making it more resilient to adversarial attacks and other vulnerabilities.

The rapid integration of the Internet of Medical Things (IoMT) is transforming healthcare through real-time monitoring, AI-driven diagnostics, and remote treatment. However, the growing reliance on IoMT devices, such as robotic surgical systems, life-support equipment, and wearable health monitors, has expanded the attack surface, exposing healthcare systems to cybersecurity risks like data breaches, device manipulation, and potentially life-threatening disruptions. While 6G networks offer significant benefits for healthcare, such as ultra-low latency, extensive connectivity, and AI-native capabilities, as highlighted in the ITU 6G (IMT-2030) framework, they are expected to introduce new and potentially more severe security challenges. These advancements put critical medical systems at greater risk, highlighting the need for more robust security measures. This study leverages AI techniques to systematically identify security vulnerabilities within 6G-enabled healthcare environments. Additionally, the proposed approach strengthens AI-driven security through use of multiple XAI techniques cross-validated against each other. Drawing on the insights provided by XAI, we tailor our mitigation strategies to the ITU-defined 6G usage scenarios, with a focus on their applicability to medical IoT networks. We propose that these strategies will effectively address potential vulnerabilities and enhance the security of medical systems leveraging IoT and 6G networks.

This decade has witnessed the initiation of the digital revolution, as anticipated with the advent of 5G networks. Looking ahead to the 6G communication era, considerations are being made regarding how individuals will engage with the digital virtual world. The design of 6G technology, which will present enormous opportunities to develop and enhance human potential, will have a major impact on communications in the 2030s. We believe that in 6G we will see an unprecedented transformation that will set it apart from earlier wireless cellular network generations. Specifically, 6G will leverage ubiquitous AI services ranging from the network's core to its end devices, going beyond unpredictable limits. Despite the numerous advantages offered by 6G over existing technologies, there remains a pressing need to address security concerns. For example, the automation of critical processes in the 6G infrastructure will lead to a significantly broader and more intricate attack surface. Thus, the significance of Artificial Intelligence (AI) in providing security aspects within the envisioned 6G paradigm is substantial, but its integration presents a dual-edged dynamic. Therefore, to strengthen and validate the relevance of AI in securing 6G networks, this article elucidates how AI can be strategically used in 6G security, addressing potential challenges, and proposing solutions to enhance its role in securing networks.

Blockchain technology's foundation of development and adaptability is one of its most fascinating features. It was introduced through Bitcoin and now has evolved into a versatile technology with applications in various industries, including healthcare. People typically associate blockchain with fintech, banking, and, of course, cryptocurrency but thanks to the technology's traceability, transparency, security, and decentralization, it can optimize and advance the healthcare industry. The medical industry has suffered greatly because of the inability to securely transmit and access sensitive patient data. Blockchain can significantly contribute to addressing these issues by facilitating faster access to reliable information, enabling safe permissioned data sharing across various stakeholders, enhancing cooperation, and raising transparency. Blockchain can also enable highly configurable openness while retaining the highest security standards for genuine interoperability. Being a relatively recent breakthrough, the application of blockchain in medical sector is still being underexplored for many of its potential applications. Before a healthcare blockchain is widely embraced, several technological issues need to be solved. This study will thoroughly analyze the cutting-edge blockchain-based healthcare technology. Alongside this, it will highlight the body of research on blockchain technology's prospective uses in the healthcare sector. Additionally, we examined the current research problems, difficulties, and drawbacks, as well as open questions, and potential areas for future research with respect to blockchain in health sectors. <!----> <!----> <!----> <!---->