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Defending Against Cyber Threats in the Smart Healthcare Ecosystem
Paperback
Business GeneralComputer Security
ISBN13: 9781041250036
Publisher: CRC Press
Published: Nov 25 2026
Pages: 332
Weight: 1.34
Height: 0.73 Width: 7.00 Depth: 10.00
Language: English
This book offers a practical guide to securing smart Internet of Things (IoT)-based healthcare devices against cyber threats through hands-on tools, threat modeling, risk scoring, and artificial intelligence (AI)-enhanced defenses.
Healthcare is undergoing rapid digital transformation, with smart IoT devices from wearable monitors to implantable sensors integrated deeply into patient care and hospital workflows. While these innovations improve efficiency and outcomes, they also introduce new and severe cyber threats. Attackers now exploit vulnerabilities in IoT firmware, protocols, and networks, leading to risks that threaten patient safety, privacy, and trust. This book provides a practical, cyber-threat-focused guide to securing healthcare IoT environments. Unlike existing titles that emphasize general healthcare IT or clinical applications of AI, this volume focuses squarely on the cybersecurity challenges of IoT medical systems and shows how AI techniques can be leveraged to target and mitigate these threats. The chapters progress logically from real-world threat incidents to architectural vulnerabilities and then to applied AI-based detection techniques. Readers are guided through:
- Analyzing IoT-specific threats in healthcare environments.
- Identifying vulnerabilities in devices and communication protocols.
- Using AI/machine learning (ML) methods for intrusion and malware detection.
- Applying threat modeling frameworks with AI-based risk scoring.
- Implementing federated learning (FL) to preserve patient privacy in collaborative detection.
- Designing Zero Trust architectures (ZTAs) adapted to healthcare IoT networks.
- Applying explainable and ethical AI methods for transparent security decision-making.
Each chapter combines conceptual depth with practical labs, datasets, and exercises. Readers build hands-on skills using tools such as TensorFlow, PyTorch, Flower, Wireshark, and Nmap, and experiment with benchmark datasets such as CICIDS2017, BoT-IoT, and CVE/National Vulnerability Database (NVD) vulnerabilities. By bridging theory with practice, this book serves as a professional reference for cybersecurity practitioners, a hands-on textbook for postgraduate courses, and a research guide for academics. Its dual orientation ensures relevance in industry, research, and education, preparing readers to secure the digital frontiers of modern healthcare.
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