Security Implementation In Internet Of Medical Things
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Security Implementation in Internet of Medical Things
Security implementation is crucial in the Internet of Medical Things (IoMT) as it ensures the protection of sensitive medical data and prevents unauthorized access to or manipulation of devices and systems. This book covers different aspects of security implementations and challenges in IoMT and aims to bring researchers together to contribute their findings to recommend new methodologies and feasible solutions for implementing security and novel architectures in artificial intelligence, machine learning, and data science in the field of healthcare and IoT. IoMT includes a wide range of connected medical devices and systems, such as wearable devices, medical sensors, and electronic health records, that collect, store, and share sensitive medical information. Without proper security measures, this information could be compromised, leading to serious privacy breaches, financial fraud, and even physical harm to patients.
Security and Privacy Issues in Internet of Medical Things
Security and Privacy Issues in Internet of Medical Things addresses the security challenges faced by healthcare providers and patients. As IoMT devices are vulnerable to cyberattacks, and a security breach through IoMT devices may act as a pathway for hackers to enter hospital networks, the book covers a very timely topic. The incorporation of blockchain in the healthcare environment has given birth to the Internet of Medical Things (IoMT), which consists of a collection of healthcare systems that espouse groundbreaking technologies. Systems consist of inter-linked sensors, wearable technology devices and clinical frameworks that perform explicit, secure machine-to-machine and cloud platform communications. The significance of IoMT in the field of healthcare is undoubtedly a win-win situation for patients through technology enhancements and a collection of analytics that helps in better diagnosis and treatment. Due to higher accuracy levels, IoMT devices are more reliable in reporting and data tracking and help avoid human errors and incorrect reporting. - Provides methods for constructing novel IoMT architectures and middleware services for healthcare applications to protect and secure patient data and privacy - Presents readers with information security and privacy models for IoMT, including Artificial Intelligence and Deep Learning, Data Storage security, Cloud, Fog and Edge computing security, and Wireless sensor device security - Provides readers with case studies for real-world applications of IoMT security, including risk assessment for IoMT, Ethical issues in IoMT, Security assessment frameworks, and Threat-based security analysis for IoMT
Artificial Intelligence for the Internet of Health Things
This book discusses research in Artificial Intelligence for the Internet of Health Things. It investigates and explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in design, implementation, and optimization of challenging healthcare solutions. This book features a wide range of topics such as AI techniques, IoT, cloud, wearables, and secured data transmission. Written for a broad audience, this book will be useful for clinicians, health professionals, engineers, technology developers, IT consultants, researchers, and students interested in the AI-based healthcare applications. Provides a deeper understanding of key AI algorithms and their use and implementation within the wider healthcare sector Explores different disease diagnosis models using machine learning, deep learning, healthcare data analysis, including machine learning, and data mining and soft computing algorithms Discusses detailed IoT, wearables, and cloud-based disease diagnosis model for intelligent systems and healthcare Reviews different applications and challenges across the design, implementation, and management of intelligent systems and healthcare data networks Introduces a new applications and case studies across all areas of AI in healthcare data K. Shankar (Member, IEEE) is a Postdoctoral Fellow of the Department of Computer Applications, Alagappa University, Karaikudi, India. Eswaran Perumal is an Assistant Professor of the Department of Computer Applications, Alagappa University, Karaikudi, India. Dr. Deepak Gupta is an Assistant Professor of the Department Computer Science & Engineering, Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India.