Cybersecurity in Emerging Healthcare Systems
Produktdetaljer
Om bidragsyterne
Agbotiname Lucky Imoize is a lecturer at the Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Nigeria. His research interests include communications networks, IoT devices, and security systems for 5G and 6G wireless communications and healthcare systems. He is a Fulbright research fellow, the vice chair of the IEEE Communication Society, Nigeria chapter, and a registered engineer. He is a senior member of the IEEE. Chandrashekhar Meshram is an assistant professor with the Department of Post Graduate Studies and Research in Mathematics, Jaywanti Haksar Government Post Graduate College, Raja Shankar Shah University, India. His research interests include cryptography, wireless communications, and network security. Joseph Bamidele Awotunde is a lecturer in the Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Nigeria. His research interests include cyber security, m-Health/e-Health systems, IoMT bioinformatics, and wireless body sensor networks. Yousef Farhaoui is a professor at the Department of Computer Science, Faculty of Sciences and Technic, Moulay Ismail University, Morocco. His research interests include cyber security and computer networks. He is the general chair, session chair, and panellist in several conferences. He is a senior member of IEEE. Dinh-Thuan Do is an assistant professor at the School of Engineering, University of Mount Union, Ohio, USA. His research interests include 6G wireless communication and AI-based Internet of Things (IoT) systems. He was a recipient of the Golden Globe Award from the Vietnamese Ministry of Science and Technology in 2015 (Top ten excellent scientists nationwide). He is a senior member of the IEEE.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.
This edited book presents various cyber security risks and vulnerability models of different healthcare systems and discusses how the application of artificial intelligence and machine learning-based solutions may help to address cyberattacks in these emerging systems.
- 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