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Full Description
Emerging healthcare networks are interconnected physical systems that use cyber technologies for interaction and functionality. The proliferation of massive internet-of-things (IoT) devices enables remote and distributed access to cutting-edge diagnostics and treatment options in modern healthcare systems. New security vulnerabilities are emerging due to the increasing complexity of the healthcare architecture, in particular, threats to medical devices and critical infrastructure pose significant concerns owing to their potential risks to patient health and safety. In recent times, patients have been exposed to high risks from attacks capable of disrupting critical medical infrastructure, communications facilities, and services, interfering with medical devices, or compromising sensitive user data.
This book seeks to present cyber risk and vulnerability models, considering a number of threats and examining how effective regulations could help guarantee medical device fidelity and trust. The book discusses the application of artificial intelligence and machine learning to provide practical learning-based solutions to address cyberattacks in emerging healthcare systems. The book focuses on the technical considerations, potential opportunities, critical cybersecurity challenges, the prospects and potential benefits of cybersecurity in emerging healthcare systems. Finally, the book presents case studies, highlighting critical lessons, and providing recommendations for designing AI-based cybersecurity architectures for emerging healthcare systems.
Written by an international team of authors, this book is suitable for an audience of industry-based and academic researchers, scientists, and computer engineers working in data science, cybersecurity and wireless communications particularly those specialising in healthcare data science and those in related fields.
Contents
Chapter 1: An overview of cybersecurity in emerging healthcare systems
Chapter 2: Cybersecurity in the Internet of Medical Things for healthcare applications
Chapter 3: Adaptive cybersecurity: AI-driven threat intelligence in healthcare systems
Chapter 4: Emerging trends in cybersecurity applications in healthcare systems
Chapter 5: Convolutional neural networks enabling the Internet of Medical Things: security implications, prospects, and challenges
Chapter 6: Deadly cybersecurity threats in emerging healthcare systems
Chapter 7: Artificial intelligence for secured cybersecurity in emerging healthcare systems
Chapter 8: Deep based anomalies detection in emerging healthcare system
Chapter 9: Smart contracts for automated compliance in healthcare cybersecurity
Chapter 10: Cybersecurity computing in modern healthcare systems
Chapter 11: Blockchain for secured cybersecurity in emerging healthcare systems
Chapter 12: The ethics of cybersecurity in emerging healthcare systems
Chapter 13: Examining the complex interactions between cybersecurity and ethics in emerging healthcare systems
Chapter 14: Securing modern insulin pumps with iCGM system: protecting patients from cyber threats in diabetes management
Chapter 15: Artificial intelligence and machine learning for DNS traffic anomaly detection in modern healthcare systems
Chapter 16: Harnessing edge computing for real-time cybersecurity in healthcare systems
Chapter 17: Enhancing healthcare data security: an intrusion detection system for web applications with SVM and decision tree algorithms
Chapter 18: Legal and regulatory policies for cybersecurity and information assurance in emerging healthcare systems
Chapter 19: Federated learning for enhanced cybersecurity in modern digital healthcare systems
Chapter 20: Directed acyclic graph-based blockchains for enhanced cybersecurity in the Internet of Medical Things
Chapter 21: Detection and mitigation of cyber attacks in healthcare systems
Chapter 22: Cybersecurity concerns and risks in emerging healthcare systems
