In the era of digital healthcare transformation, the integration of the Internet of Medical Things (IoMT) has ushered in unprecedented opportunities for enhanced patient care and medical advancements. However, this connectivity comes with the looming specter of cybersecurity threats that could compromise patient data and, in some cases, even endanger lives. This chapter delves into the intricate landscape of cybersecurity within the IoMT for healthcare applications. We explore the multifaceted challenges posed by cyber threats in the healthcare sector, where the stakes are higher than ever. From safeguarding sensitive patient information to protecting the integrity of medical devices, we dissect the evolving threat landscape and the vulnerabilities specific to the IoMT ecosystem. Moreover, we unravel the strategies and technologies employed to fortify the defenses of healthcare systems against an ever-evolving array of cyber threats. Drawing from real-world case studies and best practices, we shed light on how cutting-edge cybersecurity measures can be seamlessly integrated into IoMT environments without compromising the agility and efficiency required for modern healthcare.

As healthcare organizations rapidly adopt connected technologies and amass troves of sensitive patient data, they have become prime targets for increasingly sophisticated cyberattacks. This comprehensive study investigates the growing synergy between cybersecurity and healthcare in an era of heightened digitalization and cyber threats. It underscores the alarming vulnerabilities introduced by digitization across patient care, medical facilities, and administrative systems. Through real-world case analyses, it delineates the mounting risks of disruptive intrusions, privacy violations, and systemic compromises that jeopardize patient safety and public health. Accordingly, the study surveys cutting-edge technologies poised to harden healthcare cybersecurity. It examines the potential of artificial intelligence and ML to predict, detect, and autonomously respond to threats. The viability of blockchain, quantum computing, and zero trust frameworks in strengthening data protection and identity management are analyzed. The critical importance of security awareness training and actionable threat intelligence is also explored. Moreover, the study elucidates the complex challenges of integrating these robust yet often complex technologies into intricate clinical environments and workflows. It offers insights into how healthcare organizations can balance improved cybersecurity with user experience, regulations, legacy systems, and resource constraints. With cyber threats growing in lockstep with healthcare digitization, this study delivers a multifaceted analysis to help medical practitioners and administrators secure critical healthcare systems. It provides technology leaders a guide to emerging innovations for healthcare cybersecurity. Policymakers and regulators will benefit from its scrutiny of existing gaps.

The convergence of digital technology and healthcare has ushered in unprecedented opportunities for improved patient care and operational efficiency. However, this transformation also exposes the healthcare system to a myriad of deadly cybersecurity threats, jeopardizing patient privacy, data integrity, and overall system resilience. This chapter investigates the landscape of emerging cyber threats within the healthcare sector and presents innovative solutions to mitigate these risks. Central to our findings is the proposal for the creation of a secure data-sharing platform tailored specifically for healthcare providers. This platform not only facilitates seamless information exchange but also integrates robust security measures to safeguard against cyberattacks. By addressing this critical research gap, our report aims to bolster the cyber security posture of the emerging healthcare system, ensuring the continuity of care and the protection of sensitive patient information in an increasingly digitized world. This will benefit not only the patient but also healthcare providers.

The Internet of Things (IoT) has experienced widespread adoption, revolutionizing the way tasks are performed remotely, much like their physical counterparts. Its applications span various domains, including smart home automation, healthcare IoT, education, agriculture, smart grid, industrial IoT, security, and surveillance, among others. Our primary focus is on IoT's role in healthcare, highlighting the critical importance of robust security measures. IoT enables remote patient monitoring, timely diagnostics, and personalized recommendations. Given the online interaction between remote patients and healthcare providers, ensuring the integrity and confidentiality of diagnostic data is paramount. Inadequate security in communication channels could potentially lead to alterations in patient treatment or medication plans, posing risks of emergencies or unintended consequences. Hence, implementing robust measures to safeguard the multifaceted healthcare data generated from various sources is imperative. We particularly emphasize the significance of time series data within the Internet of Medical Things (IoMT). To address this, we leverage the Long Short-Term Memory (LSTM) approach for performance evaluation, comparing its results with stack ensemble methods. Utilizing the WUSTL Enhanced Healthcare Monitoring System (EHMS) dataset for validation, which comprises biometric and network features, totaling over 16,318 samples categorized into normal and attack categories. Additionally, we explore the most distinctive features using the Extra Trees Classifier to minimize computational complexity and enhance abnormality detection rates using ensemble algorithms. Various evaluation metrics, including accuracy, F1-score, precision, recall, and confusion matrices, are employed. Both deep learning and stack ensemble methods demonstrate notable performance across these metrics, achieving minimal false rates. Consequently, our proposed model surpasses existing machine learning algorithms, firmly establishing its effectiveness in safeguarding the security and integrity of emerging healthcare data within the IoT ecosystem.

Although the integration of digital technology into modern healthcare systems has revolutionised the administration of medical services, these systems are now susceptible to unprecedented cybersecurity vulnerabilities. In response to these challenges, machine learning-based algorithms have emerged as a powerful instrument for enhancing cybersecurity defences in the healthcare industry. This study explores the application of machine learning (ML) techniques such as K-nearest neighbours (KNN), random forest (RF), and linear regression (LR) for cybersecurity computing in emerging healthcare systems to solve cybersecurity problems in developing healthcare systems. This study provides a thorough analysis of the possible advantages of cybersecurity computing for enhancing healthcare systems. It focuses on how technology could change healthcare operations like drug development, customised medication, gene sequencing, health imaging, and so on. More significantly, this study uses ML-inspired algorithms to perform some data analytics on the security of data accessible in healthcare systems and includes a use case to illustrate the effectiveness of cybersecurity computing in emerging healthcare systems. The study begins by outlining the unique cybersecurity challenges and threats that nascent healthcare systems face, such as the abundance of networked medical devices, the complexity of healthcare information technology (IT) infrastructures, and the ever-changing landscape of threats. To anticipate the quantity of data and output file sizes needed to complete health-related tasks, LR, RF algorithms, and KNN algorithms are employed. This allows for the identification of odd patterns or behaviours that may indicate security infractions. Mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) are performance metrics used for the evaluation and comparison of ML algorithms. In general, the KNN algorithm in some cases outperforms other algorithms such as LR and RF when compared using MSE with numerical values of 833.6, 662.04, and 833.6, respectively.

This chapter delves into the ethics of cybersecurity in emerging healthcare systems, examining the complex relationship between ethical considerations and technological advancements. We begin by defining evolving healthcare systems and then highlight the significance of cybersecurity in preserving trust among patients. We examine the benefits and drawbacks of integrating technology while highlighting the delicate balance that must be drawn between innovation and data security. When it comes to patient autonomy, informed consent, and responsible data sharing for investigation, ethical considerations are crucial. A thorough analysis of the legal framework is conducted, with a focus on the challenges associated with enforcing cybersecurity regulations in rapidly evolving healthcare systems. We analyse the ethical ramifications of AI and data breaches in the healthcare industry, promoting human oversight, fairness, and precautionary practices. In addition to providing a comprehensive road map for navigating this rapidly changing environment, the chapter concludes with a call to action that urges stakeholders to give ethical cybersecurity practices priority when creating and implementing new healthcare systems.

Managing diabetes is a demanding task that requires constant attention and effort. From monitoring blood glucose levels to administering insulin doses, individuals with diabetes face a daily regimen that can be particularly challenging. However, recent advancements in technology have introduced innovative solutions aimed at simplifying this process and improving the quality of life for those affected by the condition. One such advancement is the integration of continuous glucose monitoring (CGM) systems with insulin pumps, collectively known as integrated continuous glucose monitoring (iCGM) systems. These devices offer users the convenience of automated blood glucose monitoring and insulin administration, reducing the need for constant manual intervention. Moreover, modern iCGMs provide additional features such as connectivity with smart devices, allowing users to monitor their health data in real time. While these advancements undoubtedly offer numerous benefits, they also come with inherent risks, particularly concerning security vulnerabilities. The reliance on interconnected devices opens the door to potential exploitation, which could have serious consequences for individuals with diabetes. For instance, a security breach could lead to insulin overdose, resulting in severe hypoglycemia or even life-threatening complications such as severe brain injuries, coma, or even death. The architecture of iCGM systems has evolved rapidly in recent years, with a focus on compact, wearable designs that rely heavily on Bluetooth technology for connectivity. While Bluetooth provides basic security features, such as encryption, device authentication, and access control, the responsibility for implementing additional security measures such as user authentication falls on the device manufacturers. Unfortunately, this reliance on external security mechanisms and unavailable security features leaves iCGM systems vulnerable to cyber threats. Despite some efforts to address security concerns in iCGM systems, they have not undergone thorough scrutiny for practical implementation. Furthermore, existing recent works have not comprehensively covered all vulnerabilities, indicating a significant research gap in this area. This chapter outlines the modern architecture of insulin pumps, existing vulnerabilities, threats, and risks of iCGM systems, and provides insights on security measures, mitigation, and countermeasures. The goal is to bridge the research gap by identifying current architecture and threats while highlighting necessary security mechanisms. Furthermore, the chapter provides resources of open-source datasets for further research and testing to secure iCGM systems.

This book chapter presents an in-depth exploration of the integration of Edge Computing with cybersecurity within healthcare systems, a critical development in the era of digital healthcare transformation. As the Internet of Medical Things (IoMT) continues to expand, generating vast amounts of data, traditional cloud-centric models are becoming increasingly insufficient. These models, while once groundbreaking, now struggle to meet the urgent demands for low-latency and high-security data processing essential in healthcare applications. The chapter advocates for a paradigm shift toward Edge Computing architectures. Unlike conventional models, Edge Computing processes data at the network's edge, closer to where it is generated. This proximity significantly reduces response times and bandwidth usage, crucial for real-time medical data analysis and prompt decision-making in patient care. The chapter meticulously dissects the design, implementation challenges, and potential of edge-based cybersecurity frameworks. It emphasizes how these frameworks can leverage distributed data processing to enhance threat detection and mitigation, ensuring robust protection against cyber threats. In-depth case studies are presented, illustrating successful implementations of Edge Computing in enhancing cybersecurity in healthcare settings. These real-world examples serve as a blueprint for future implementations, demonstrating practical applications and the tangible benefits of Edge Computing in healthcare. Furthermore, the chapter delves into the broader implications of this technological shift. It discusses the evolving landscape of healthcare infrastructure, the need for adaptive strategies to accommodate these advanced technologies, and the role of Edge Computing in facilitating secure, efficient, and patient-centric healthcare services. By highlighting the synergies between Edge Computing and cybersecurity, the chapter underscores the critical need for healthcare systems to evolve and adapt. It presents Edge Computing not just as a technological innovation but as a strategic necessity to address modern healthcare challenges, particularly in cybersecurity and data management.

Protecting data and information systems from unwanted access, use, change, or destruction is known as cybersecurity. The healthcare sector is among the most critical and challenging domains for cybersecurity. Healthcare systems comprise numerous entities and stakeholders, such as clinics, hospitals, laboratories, pharmacies, insurance companies, patients, and medical personnel. For these institutions to deliver efficient and superior healthcare services, including diagnosis, treatment, prevention, and research, they rely on information technology and data. However, these systems and data are also vulnerable to cyberattacks, which can endanger the security and well-being of patients and healthcare providers as well as the privacy, accuracy, and accessibility of medical records. Cyberattacks against healthcare systems can lead to several dangerous consequences, including ransomware, malware, sabotage, espionage, data breaches, identity theft, and fraud. These attacks may affect a country's general health and national security in addition to the targeted victims. As a result, it is imperative to ensure the information assurance and cybersecurity of developing healthcare systems using legislative and regulatory frameworks that take risk management, actual situations, and emerging trends into account.

To strengthen cybersecurity in the Internet of Medical Things (IoMT), this chapter examines the use of blockchains based on Directed Acyclic Graphs (DAGs). Securing private medical data and guaranteeing the integrity of networked medical devices become critical challenges as the IoMT scenario changes. The chapter leverages the efficiency and resilience of DAG-based blockchains against certain attacks. The proposed access control approach is built on the decentralised and impenetrable structure of Tangle, a DAG-based consensus technique. A novel framework for access control is presented, which represents IoMT entities with DAG structures. Directed edges encode dynamic relationships and permissions, whereas nodes represent medical equipment, healthcare providers, and patients. Blockchain-integrated smart contracts automate access decisions, guaranteeing adherence to healthcare laws and enabling real-time modifications to access rights. Security considerations encompass encryption protocols for secure communication, decentralised identity solutions for authentication, and immutable audit trails to meet regulatory requirements. To facilitate interoperability and smooth data sharing, the chapter puts a strong emphasis on integrating the access control mechanism with the current healthcare systems. The proposed solution aims to revolutionise IoMT cybersecurity by addressing the particular difficulties faced by the healthcare industry, namely security, and scalability. Furthermore, metrics like Elapsed Time vs. Number of Nodes, Access Rate vs. Number of Nodes, and Throughput vs. Number of Nodes are used to evaluate the implementation of the suggested access control, demonstrating observable improvements. The chapter emphasises how DAG-based blockchains might improve IoMT cybersecurity in a revolutionary way. This study enhances the security of developing healthcare systems amid the growth of linked medical devices and digital health technologies by providing a clear and flexible access control mechanism.

The adoption of new technologies by healthcare systems brings with it new risks related to cybersecurity and new concerns. This chapter investigates a comprehensive strategy for reducing these risks in healthcare institutions by looking into the foundation of a thorough cybersecurity strategy, which consists of risk assessments, frequent security audits, strong access controls, and data encryption. Emerging healthcare systems leverage on advanced technologies to enhance patient care, streamline operations, and improve efficiency. In addition, the study examines the cybersecurity challenges facing emerging healthcare systems, focusing on the vulnerabilities inherent in interconnected medical devices, electronic health records, and telehealth platforms. The chapter explores the potential consequences of cyberthreats, including data breaches, privacy violations, and disruptions to patient care. Furthermore, the study discusses strategies to mitigate these risks, such as implementing robust encryption protocols, adopting multi-factor authentication, and enhancing employee cybersecurity awareness through a comprehensive training. By understanding and addressing these cybersecurity concerns, emerging healthcare systems can better safeguard their patient data and maintain the integrity of their operations.