Keshav Sinha – författare

This book offers a practical guide to securing smart IoT-based healthcare devices against Cyberthreats with hands-on tools, threat modelling, risk scoring, and AI-enhanced defences.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/ML methods for intrusion and malware detection.Applying threat modeling frameworks with AI-based risk scoring.Implement federated learning to preserve patient privacy in collaborative detection.Designing Zero Trust architectures adapted to healthcare IoT networks.Apply 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/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.

This book offers a practical guide to securing smart IoT-based healthcare devices against Cyberthreats with hands-on tools, threat modelling, risk scoring, and AI-enhanced defences.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/ML methods for intrusion and malware detection.Applying threat modeling frameworks with AI-based risk scoring.Implement federated learning to preserve patient privacy in collaborative detection.Designing Zero Trust architectures adapted to healthcare IoT networks.Apply 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/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.