AI-Driven Cybersecurity for Intelligent Healthcare Systems
- 1st Edition - September 1, 2026
- Latest edition
- Editors: Balamurugan Balusamy, Prithi Samuel, Sunita Chand, Mahmoud Ahmad Al-Khasawneh
- Language: English
AI-Driven Cybersecurity for Intelligent Healthcare Systems explores the intersection between AI, cybersecurity, and healthcare. The book offers detailed insights into the unique… Read more
Description
Description
AI-Driven Cybersecurity for Intelligent Healthcare Systems explores the intersection between AI, cybersecurity, and healthcare. The book offers detailed insights into the unique cybersecurity challenges faced by the healthcare sector and the role of AI in addressing these challenges. It presents case studies and real-world applications to illustrate the effectiveness of these solutions and highlights the significance of data privacy in healthcare and methods to ensure secure data sharing and storage. Topics such as federated learning, homomorphic encryption, and blockchain technology are covered to demonstrate how AI can enhance data security without compromising patient privacy.
This book will be an essential resource for anyone involved in the healthcare industry, offering practical solutions and fostering a more in-depth understanding of how AI can revolutionize cybersecurity in healthcare.
This book will be an essential resource for anyone involved in the healthcare industry, offering practical solutions and fostering a more in-depth understanding of how AI can revolutionize cybersecurity in healthcare.
Key features
Key features
- Includes case studies and real-world applications to illustrate the effectiveness of intelligent cybersecurity solutions
- Discusses the increasing integration of artificial intelligence (AI) in healthcare systems, highlighting its role in enhancing medical diagnosis, treatment planning, and patient care
- Focuses on advanced techniques for enhancing data security while maintaining patient privacy
Readership
Readership
Researchers and Academics in fields such as biomedical engineering, computer science, information technology, and healthcare informatics who study the intersection of AI, cybersecurity, and healthcare; graduates and undergraduates in disciplines such as healthcare management, health informatics, computer science, and cybersecurity looking to deepen their understanding of these areas
Table of contents
Table of contents
1. Transforming healthcare cybersecurity from reactive to proactive: current status and future recommendations
1.1 State of cybersecurity & cyber threats in healthcare organizations
1.2 Classification of challenges and threats in healthcare cybersecurity
1.3 Healthcare information technologies in an era of healthcare reform: A complex adaptive system perspective
2. Transforming healthcare: harnessing the power of AI in the modern era
2.1 Cybersecurity challenges in healthcare
2.2 Clinicians' perspectives on healthcare cybersecurity and cyber threats
2.3 The Rise of the AI Ecosystem in Healthcare
3. Machine Learning-Enhanced Threat Intelligence for Advanced Cybersecurity
3.1 Optimal machine learning algorithms for cyber threat detection
3.2 The role of real-time threat analytics, mitigating data integrity risks; within the iomt healthcare environment
3.3 Healthcare information technologies in an era of healthcare reform: A complex adaptive system perspective
4. Security of blockchain and AI-empowered smart healthcare: application-based analysis
4.1 Healthcare applications using blockchain technology: Motivations and challenges
4.2 Secure electronic medical records storage and sharing using blockchain technology
4.3 A readiness assessment framework for Blockchain adoption: A healthcare case study
5. Artificial Intelligence of Internet of Medical Things (AIoMT) Cybersecurity for Smart Healthcare in Cities
5.1 Security vulnerabilities, attacks, countermeasures, and regulations of networked medical devices
5.2 Design challenges for secure implantable medical devices
5.3 An internet of things (IoT)‐based optimization to enhance security in healthcare applications
6. Cybersecurity Strategies for Multi-Cloud Healthcare Using Deep Learning
6.1 Cloud Computing in Healthcare: Opportunities, Risks, and compliance
6.2 Encryption algorithm for data security and privacy in cloud storage
6.3 Utilizing hybrid cloud strategies to enhance data storage and security in e-commerce applications
7. Intelligent Access Control Solutions for Healthcare Cybersecurity
7.1 A context-aware security model for a combination of attribute-based access control and attribute-based encryption in the healthcare domain
7.2 Privacy-preserving multi-factor authentication and role-based access control scheme for the E-healthcare system
7.3 A AI driven security awareness and protection system for 5G smart healthcare based on zero-trust architecture
8. AI-driven threat detection and response: A paradigm shift in cybersecurity
8.1 Leveraging Machine Learning for Intelligent Healthcare and Cybersecurity in Society 5.0
8.2 Cyber Resilience through Real-Time Threat Analysis in Information Security
8.3 Cyber Security: Threat Detection Model based on Machine learning Algorithm
8.4 Cyber-attack detection in healthcare using cyber-physical systems and machine learning techniques
9. Blockchain for healthcare: securing patient data and enabling trusted artificial intelligence
9.1 Healthcare applications using blockchain technology: Motivations and challenges
9.2 Securing and authenticating healthcare records through blockchain technology: Patient-centric and fine-grained data access control in multi-owner settings
9.3 Exploring Integration of Advanced Encryption Techniques with Blockchain for Healthcare Big Data Security
9.4 Blockchain in health care innovation: literature review and case study from a business ecosystem perspective
9.5 Securing Healthcare IoT with Blockchain-Based Identity Management
10. Deep Learning Techniques for Securing Data in Healthcare Applications
10.1 A conceptual model for Internet of Things risk assessment in healthcare domain with deep learning approach
10.2 Early detection of the advanced persistent threat attack using performance analysis of deep learning
10.3 Data security challenges in deep neural network for healthcare IoT systems
10.4 Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems
11. Chatbot Systems in Healthcare: AI Integration and Security Measures
11.1 Chatbots in healthcare: Status quo, application scenarios for physicians and patients and future directions
11.2 Security implications of AI chatbots in health care
11.3 AI-Powered Chatbots for Patient Education and Engagement
11.4 Understanding privacy and security postures of healthcare chatbots
12. Securing Healthcare IoT with Blockchain-Based Identity Management
12.1 An investigation of biometric authentication in the healthcare environment
12.2 User behaviour analysis using data analytics and machine learning to predict malicious user versus legitimate user
12.3 Securing personal health records in cloud computing: Patient-centric and fine-grained data access control in multi-owner settings
13. A survey on smart medical wearables in the application of fitness
13.1 Privacy and security issues of wearables in healthcare
13.2 Security threats and cryptographic protocols for medical wearables
13.3 Regulatory, legal, and market aspects of smart wearables for cardiac monitoring
14. Recent advancements in emerging technologies for secure healthcare management Systems
14.1 Foresight of evolving security threats posed by emerging technologies
14.2 AI-driven IoT for smart health care: Security and privacy issues
14.3 Emerging Cyber Risks & Threats in Healthcare Systems: A Case Study in Resilient Cybersecurity Solutions
15. Shaping the future of AI in healthcare through ethics and Governance
15.1 Implementing ethics in healthcare AI-based applications
15.2 The Impact of Bias and Fairness Issues on the Robustness and Security of AI Systems
15.3 Artificial intelligence and surgery: ethical dilemmas and open issues
15.4 Regulatory Challenges in Healthcare IT: Ensuring Compliance with HIPAA and GDPR
15.5 Regulating the Revolution: A Legal Roadmap to Optimizing AI in Healthcare
15.6 Mapping the regulatory landscape for artificial intelligence in health across the globe.
1.1 State of cybersecurity & cyber threats in healthcare organizations
1.2 Classification of challenges and threats in healthcare cybersecurity
1.3 Healthcare information technologies in an era of healthcare reform: A complex adaptive system perspective
2. Transforming healthcare: harnessing the power of AI in the modern era
2.1 Cybersecurity challenges in healthcare
2.2 Clinicians' perspectives on healthcare cybersecurity and cyber threats
2.3 The Rise of the AI Ecosystem in Healthcare
3. Machine Learning-Enhanced Threat Intelligence for Advanced Cybersecurity
3.1 Optimal machine learning algorithms for cyber threat detection
3.2 The role of real-time threat analytics, mitigating data integrity risks; within the iomt healthcare environment
3.3 Healthcare information technologies in an era of healthcare reform: A complex adaptive system perspective
4. Security of blockchain and AI-empowered smart healthcare: application-based analysis
4.1 Healthcare applications using blockchain technology: Motivations and challenges
4.2 Secure electronic medical records storage and sharing using blockchain technology
4.3 A readiness assessment framework for Blockchain adoption: A healthcare case study
5. Artificial Intelligence of Internet of Medical Things (AIoMT) Cybersecurity for Smart Healthcare in Cities
5.1 Security vulnerabilities, attacks, countermeasures, and regulations of networked medical devices
5.2 Design challenges for secure implantable medical devices
5.3 An internet of things (IoT)‐based optimization to enhance security in healthcare applications
6. Cybersecurity Strategies for Multi-Cloud Healthcare Using Deep Learning
6.1 Cloud Computing in Healthcare: Opportunities, Risks, and compliance
6.2 Encryption algorithm for data security and privacy in cloud storage
6.3 Utilizing hybrid cloud strategies to enhance data storage and security in e-commerce applications
7. Intelligent Access Control Solutions for Healthcare Cybersecurity
7.1 A context-aware security model for a combination of attribute-based access control and attribute-based encryption in the healthcare domain
7.2 Privacy-preserving multi-factor authentication and role-based access control scheme for the E-healthcare system
7.3 A AI driven security awareness and protection system for 5G smart healthcare based on zero-trust architecture
8. AI-driven threat detection and response: A paradigm shift in cybersecurity
8.1 Leveraging Machine Learning for Intelligent Healthcare and Cybersecurity in Society 5.0
8.2 Cyber Resilience through Real-Time Threat Analysis in Information Security
8.3 Cyber Security: Threat Detection Model based on Machine learning Algorithm
8.4 Cyber-attack detection in healthcare using cyber-physical systems and machine learning techniques
9. Blockchain for healthcare: securing patient data and enabling trusted artificial intelligence
9.1 Healthcare applications using blockchain technology: Motivations and challenges
9.2 Securing and authenticating healthcare records through blockchain technology: Patient-centric and fine-grained data access control in multi-owner settings
9.3 Exploring Integration of Advanced Encryption Techniques with Blockchain for Healthcare Big Data Security
9.4 Blockchain in health care innovation: literature review and case study from a business ecosystem perspective
9.5 Securing Healthcare IoT with Blockchain-Based Identity Management
10. Deep Learning Techniques for Securing Data in Healthcare Applications
10.1 A conceptual model for Internet of Things risk assessment in healthcare domain with deep learning approach
10.2 Early detection of the advanced persistent threat attack using performance analysis of deep learning
10.3 Data security challenges in deep neural network for healthcare IoT systems
10.4 Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems
11. Chatbot Systems in Healthcare: AI Integration and Security Measures
11.1 Chatbots in healthcare: Status quo, application scenarios for physicians and patients and future directions
11.2 Security implications of AI chatbots in health care
11.3 AI-Powered Chatbots for Patient Education and Engagement
11.4 Understanding privacy and security postures of healthcare chatbots
12. Securing Healthcare IoT with Blockchain-Based Identity Management
12.1 An investigation of biometric authentication in the healthcare environment
12.2 User behaviour analysis using data analytics and machine learning to predict malicious user versus legitimate user
12.3 Securing personal health records in cloud computing: Patient-centric and fine-grained data access control in multi-owner settings
13. A survey on smart medical wearables in the application of fitness
13.1 Privacy and security issues of wearables in healthcare
13.2 Security threats and cryptographic protocols for medical wearables
13.3 Regulatory, legal, and market aspects of smart wearables for cardiac monitoring
14. Recent advancements in emerging technologies for secure healthcare management Systems
14.1 Foresight of evolving security threats posed by emerging technologies
14.2 AI-driven IoT for smart health care: Security and privacy issues
14.3 Emerging Cyber Risks & Threats in Healthcare Systems: A Case Study in Resilient Cybersecurity Solutions
15. Shaping the future of AI in healthcare through ethics and Governance
15.1 Implementing ethics in healthcare AI-based applications
15.2 The Impact of Bias and Fairness Issues on the Robustness and Security of AI Systems
15.3 Artificial intelligence and surgery: ethical dilemmas and open issues
15.4 Regulatory Challenges in Healthcare IT: Ensuring Compliance with HIPAA and GDPR
15.5 Regulating the Revolution: A Legal Roadmap to Optimizing AI in Healthcare
15.6 Mapping the regulatory landscape for artificial intelligence in health across the globe.
Product details
Product details
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
About the editors
About the editors
BB
Balamurugan Balusamy
Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor in the Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, blockchain, and data sciences
Affiliations and expertise
Shiv Nadar University, Delhi-NCR, IndiaPS
Prithi Samuel
Dr Prithi Samuel is currently working as an assistant professor in the Department of Computational Intelligence at SRM Institute of Science and Technology, Kattankulathur Campus, Chennai. She has completed her Ph.D. in Information and Communication Engineering from Anna University, Chennai. She has got over 15 years of teaching experience in reputed engineering colleges in Tamil Nadu. She is a pioneer researcher in the areas of Automata Theory, Machine Learning, Deep Learning, Computational Intelligence Techniques, and the Internet of Things. She has published more than 25 papers in leading SCI and Scopus Journals and more than 25 papers in International Conferences and published 1 book and more than 10 book chapters in Wiley, Taylor and Francis, Springer, and Elsevier and published 4 patents and 2 patent grants. She is an active IEEE, ACM Member and holds an ISTE and IAENG lifetime membership
Affiliations and expertise
Assistant Professor, Department of Computational Intelligence at SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, IndiaSC
Sunita Chand
Sunita Chand is an assistant professor in the Department of Department of Computer Science, Hansraj College, University of Delhi. She is a pioneer researcher in the areas of image processing, cyber security, deep learning, natural language processing, and nature inspired algorithms. She has published papers in leading international journals and international conferences and published book chapters in IEEE, Springer, Inderscience, AIP, ACM and Elsevier.
Affiliations and expertise
Assistant Professor, Department of Computer Science, Hansraj College, University of Delhi, IndiaMA
Mahmoud Ahmad Al-Khasawneh
Mahmoud Ahmad Al-Khasawneh is a faculty member in the School of Computing Skyline University College, Sharjah UAE.
His scholarly pursuits span a diverse array of fields within computer science. He has authored numerous papers in esteemed, peer-reviewed journals across leading publishers such as IEEE, Springer, Wiley, Hindawi, and MDPI. His research interests encompass Security, Image Encryption, Wireless Networks, Blockchain, Internet of Things, and Big Data. With a commitment to advancing knowledge and solving contemporary challenges in these domains, he actively engages in research, teaching, and mentorship, contributing to the academic and professional development of his students and peers. Driven by a passion for innovation and a dedication to excellence, he continues to make significant contributions to the field, shaping the future of technology and its applications.Affiliations and expertise
Faculty, School of Computing Skyline University College, Sharjah UAE