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5G is transforming the healthcare industry by providing novel use cases and applications. With a lower latency, a higher bandwidth, and massive machine-type communication capabilities, 5G supports the smart healthcare system in expanding its applications. This rapid adoption of 5G technology increased the number of connected devices. However, the deployment of 5G networks also introduced security challenges, especially for sensitive patient data which must be secured. Base stations are a key component in connecting devices to the mobile network and need to be secured. As unauthorized base stations cause interference or can be used as fake base stations to attack devices in the network, it is crucial to address the security concerns associated with 5G-based smart healthcare networks. Thus, we studied the case of base station security to investigate different methods for detecting and mitigating the risks associated with unauthorized base stations. The results can be used to better secure 5G-based smart healthcare networks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has revolutionized technologies in society, including in households, offices, factories, and health centers. Among these, the Healthcare Internet of Things (HIoT) significantly transforms medical assistance for patients. By using wearable devices with remote network connections, caregivers monitor patients' physiological data to gain valuable insights into their health conditions. Despite the many benefits of the HIoT, several security vulnerabilities still exist. Hackers can exploit the internet connection to steal or modify credential information regarding patients, violating the integrity and confidentiality of the security policy. Moreover, they can launch cyberattacks on hospitals or critical life-support systems, further endangering patients' lives. Consequently, it is crucial to implement robust cybersecurity measures to enhance the security of healthcare services. Therefore, we proposed an anomaly detection method based on network traffic for the HIoT, adopting Markov models. Owing to their simplicity, interpretability, and well-developed theory, the Markov models have been applied to network traffic prediction and modeling, serving as a viable approach to cater to our needs. We evaluated the proposed method using the public dataset ToN_IoT and analyzed the results. [ABSTRACT FROM AUTHOR]

New mobile network technologies, particularly 5G, have spurred a growth in smart healthcare networks. They enable real-time monitoring, personalized treatments, and more. However, these transformative capabilities have also uncovered potential vulnerabilities, emphasizing the urgency to safeguard patient data and healthcare services. This study analyzes the existing research on 5G-based smart healthcare network security with a specific emphasis on fake base station attacks. The research investigates potential security measures to mitigate the impact of fake base station attacks. And based on those findings, we propose a detection scheme to help combat fake base station threats effectively and to avoid the need to install individual apps on smart devices, providing a foothold for future efforts to develop and deploy better countermeasures. To ensure a secure and resilient ecosystem for 5G-based smart healthcare, continuous research and proactive measures are required. By staying vigilant and committed to research and development, we can protect patient privacy, ensure secure data transmission, and enhance the quality of services within smart healthcare networks and other mobile network applications alike. [ABSTRACT FROM AUTHOR]