Search

Browse Subject Areas

For Authors

Submit a Proposal

Artificial Intelligence and Cybersecurity in Healthcare

Edited by Rashmi Agrawal, Pramod Singh Rathore, Ganesh Gopal Devarajan, and Rajiva Ranjan Divivedi
Series: Advances in Cyber Security
Copyright: 2025   |   Status: Published
ISBN: 9781394229796  |  Hardcover  |  
508 pages
Price: $225 USD
Add To Cart

One Line Description
Artificial Intelligence and Cybersecurity in Healthcare offers a vital exploration of the intersection of artificial intelligence and cybersecurity within healthcare Cyber Physical Systems, equipping readers with knowledge on how to navigate the transformative yet complex technological landscape shaping modern patient care and data protection.

Audience
Industry professionals, research scholars, post-graduate and undergraduate students, and policymakers working in the areas of healthcare and cybersecurity

Description
Healthcare has always been a crucial aspect of human society, and with the advancement of technology, the healthcare sector has witnessed a significant transformation. One such technological advancement is the implementation of Cyber Physical Systems (CPS) in healthcare, which combines the physical and digital world to create a more efficient and effective healthcare system. However, with an increased reliance on technology, the security of these systems has become a major concern. Artificial Intelligence and Cybersecurity in Healthcare
discusses the integration of these elements in healthcare CPS, current technological advancements, and their applications, issues, and challenges.
The application of AI and cybersecurity in Healthcare CPS has been used in various healthcare settings. For instance, remote patient monitoring systems use AI algorithms to analyze patient data and identify potential health issues. Additionally, AI chatbots are used to assist patients in scheduling appointments, answering questions, and providing medical advice. Furthermore, cybersecurity measures such as biometric authentication are used to protect patient data in electronic health records. The use of Artificial Intelligence (AI) in healthcare CPS can improve patient care and clinical decision-making by analyzing large amounts of patient data, predicting adverse events, and optimizing treatment plans. AI can also help detect and respond to cyber threats in real-time, enhancing the resilience and security of healthcare CPS. However, AI systems can be vulnerable to attacks and require robust cybersecurity measures to ensure their reliability and trustworthiness.
One of the significant concerns is the privacy and security of patient data. Healthcare data is sensitive, and a data breach can have significant consequences, including identity theft and medical fraud. Moreover, the use of AI in healthcare decision-making can lead to ethical issues, such as bias in algorithmic decision-making. The need for AI and cyber security in healthcare CPS is becoming more urgent as the healthcare industry becomes more reliant on digital technologies and data-driven approaches to patient care. Healthcare organizations need to integrate AI and cyber security measures into their CPS to ensure the safety and quality of patient care and protect against cyber threats. This requires collaboration between healthcare providers, technology vendors, regulatory agencies, and cybersecurity experts to develop and implement best practices and standards for AI and cyber security in healthcare CPS.
The purpose of Artificial Intelligence and Cybersecurity in Healthcare is to provide a comprehensive understanding of the intersection of artificial intelligence, cybersecurity, and healthcare cyber physical systems. It aims to explore the potential of advanced technologies like augmented reality, virtual reality, explainable artificial intelligence, digital trust, digital twin, blockchain technology, cloud computing, fog computing, mist computing, edge computing, and the Internet of Things in the healthcare sector. The book will also provide insights into the various challenges associated with implementing these technologies in healthcare cyber physical systems, including cybersecurity threats, ethical issues, and legal and regulatory frameworks.

Back to Top
Author / Editor Details
Rashmi Agrawal, PhD is a professor and the Head of the Department of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, India with 20 years of experience in teaching and research. She is a lifetime member of the Computer Society of India, a senior member of the Institute of Electrical and Electronics Engineers, and a chapter chair and professional member of the Association for Computing Machinery. Alongside her affiliations, she is a series editor, has authored and co-authored over 80 research papers in peer-reviewed national and international journals and conferences, and has four patents to her credit as well as a copyright. Additionally, she has contributed as a keynote speaker at IEEE international conferences, an expert lecturer at professional development events, and a session chair for various international conferences.

Pramod Singh Rathore, PhD, is currently working as an Assistant professor of Department of Computer and Communication Engineering, Manipal University Jaipur. He is Doctorate in Computer Science & Engineering from University of Engineering and Management, Jaipur (UEM) and done M. Tech in Computer Sci. & Engineering from Government engineering college Ajmer, Rajasthan Technical University, Kota India. With over 12 years of academic teaching experience, he has published more than 85 papers in reputable, peer-reviewed national and international journals, books, and conferences, including Wiley, IGI GLOBAL, Taylor & Francis Springer, Elsevier Science Direct, Annals of Computer Science, Poland, and IEEE. He has co-authored and edited numerous books with well-known publishers such as Wiley, CRC Press, and USA. His research interests include NS2, computer networks, mining, and DBMS. He serves on the editorial and advisory committees of the Global Journal Group and is also a member of various national and international professional societies in the field of engineering and research, including the ACM, International Association of Engineers (IAENG).

Ganesh Gopal Devarajan, PhD is a professor in the Department of Computer Science and Engineering, SRM Institute of Science and Technology, India with more than 17 years of research and teaching experience in computer science and engineering. He has edited many special issues in reputed journals and is a member of the Institute of Electrical and Electronics Engineers, Association for Computing Machinery, and Computer Society of India. His research interests include Internet of Things (IoT), wireless communication, vehicular communication, and big data.

Rajiva Ranjan Divivedi is an assistant professor in the Computer Science and Engineering Department at SRM Institute of Science and Technology, Delhi, India with over six years of teaching and research experience. He holds a Master's Degree in Computer Science and Engineering and has qualified under both the National Testing Agency’s National Eligibility Test and the Graduate Aptitude Test in Engineering. His research interests include machine learning, data analytics, and Internet of Things.

Back to Top

Table of Contents
Foreword
Preface
1. Digital Prescriptions for Improved Patient Care are Transforming Healthcare Through Voice-Based Technology
Preeti Narooka and Deepa Parasar
1.1 Introduction
1.2 Literature Review
1.2.1 Research Paper Survey
1.2.2 Existing System Methodologies
1.2.3 Comparative Analysis
1.2.3.1 Google Cloud Speech-to-Text API
1.2.3.2 Microsoft Azure Speech Services
1.2.3.3 IBM Watson Speech to Text
1.2.3.4 CMU Sphinx
1.3 Proposed System
1.4 Implementation and Results
1.5 Conclusion
References
2. Securing IoMT-Based Healthcare System: Issues, Challenges, and Solutions
Ashok Kumar, Rahul Gupta, Sunil Kumar, Kamlesh Dutta and Mukesh Rani
2.1 Introduction
2.1.1 Motivation for the Study
2.2 Related Work
2.3 SHS Architecture, Applications, and Challenges
2.3.1 Applications of the Smart Healthcare System
2.3.2 Open Key Challenges
2.4 Security Issues in SHS
2.5 Security Solutions/Techniques Proposed by Researchers
2.6 Future Research Directions
2.7 Conclusion
References
3. Fog Computing in Healthcare: Enhancing Security and Privacy in Distributed Systems
Deepa Arora and Oshin Sharma
3.1 Introduction
3.1.1 Applications of Fog Computing in Healthcare
3.1.2 Technical Details of Implementing Fog Computing in Healthcare System
3.2 Case Studies
3.2.1 Case Study 1: Remote Monitoring of Patients Using Fog Computing
3.2.2 Case Study 2: Fog Computing in Clinical Decision Support
3.2.3 Case Study 3: Smart Health 2.0 Project in China
3.3 Challenges
3.4 Methods to Enhance Security and Privacy in Distributed Systems
3.5 Future Directions of Fog Computing in Healthcare
3.6 Conclusion
References
4. Blockchain Technology for Securing Healthcare Data in Cyber-Physical Systems
Himanshu Rastogi, Abhay Narayan Tripathi and Bharti Sharma
4.1 What is Healthcare Data?
4.1.1 Technologies in Healthcare
4.1.1.1 IoT for Healthcare
4.1.1.2 Online Healthcare
4.1.1.3 Big Data in Healthcare
4.1.1.4 Artificial Intelligence in Healthcare
4.2 Need of Maintaining Healthcare Data
4.3 Risk Associated with Healthcare Data
4.4 Cyber-Physical Systems (CPS)
4.5 Healthcare Cyber-Physical Systems (HCPS)
4.6 Blockchain Technology
4.6.1 Block Structure
4.6.2 Hashing and Digital Signature
4.7 Blockchain Technology in Healthcare Data
4.8 Blockchain-Enabled Cyber-Physical Systems (CPS)
4.9 Conclusion
References
5. Augmented Reality and Virtual Reality in Healthcare: Advancements and Security Challenges
Srinivas Kumar Palvadi, Pradeep K. G. M., D. Rammurthy, G. Kadiravan and M. M. Prasada Reddy
Introduction
Advancements
Security Challenges
What is Augmented Reality?
What is Virtual Reality?
Revent Developments in AR and VR
Augmented Reality in Ecommerce
Virtual Reality in Healthcare
Augmented Reality in Advertising
Virtual Reality in Education
Research Problems in AR and VR in Healthcare
User Experience
Effectiveness
Integration with Clinical Workflow
Data Security and Privacy
Cost-Effectiveness
Challenges in AR and VR in Healthcare
Data Privacy and Security
Cost
Technical Issues
Integration with Existing Systems
Training and Education
Legal and Ethical Considerations
Future Research in AR and VR
User Experience
Health Applications
Education and Training
Technical Advancements
Ethical and Legal Implications
Security Challenges in AR and VR
Data Privacy
Malware and Viruses
User Safety
Intellectual Property Theft
Cybersecurity Vulnerabilities
Social Engineering
Device and Network Security
Conclusion
References
6. Next Generation Healthcare: Leveraging AI for Personalized Diagnosis, Treatment, and Monitoring
Suraj Shukla and Brijesh Kumar
6.1 Introduction
6.2 Benefits of AI in Healthcare
6.2.1 Personalized Diagnosis and Treatment
6.2.2 Improved Diagnostic Accuracy and Speed
6.2.3 Accelerated Drug Discovery
6.2.4 Remote Monitoring and Early Detection
6.3 Challenges of AI in Healthcare
6.3.1 Data Privacy and Security
6.3.1.1 Data Encryption
6.3.1.2 Access Controls
6.3.1.3 Data Anonymization
6.3.1.4 Secure Infrastructure
6.3.1.5 Compliance with Regulations
6.3.2 Algorithmic Transparency and Interpretability
6.3.2.1 Explainable AI (XAI) Techniques
6.3.2.2 Standardized Reporting
6.3.2.3 Ethical Considerations
6.3.2.4 Regulatory Framework
6.3.3 Ethical Considerations
6.3.4 Limited Generalizability
6.3.5 Regulatory and Legal Frameworks
6.3.6 Cyber Threat
6.4 Approaches to Addressing Challenges in AI in Healthcare
6.4.1 Data Privacy and Security Measures
6.4.2 Algorithmic Transparency and Interpretability Techniques
6.4.3 Ethical Frameworks and Guidelines
6.4.4 Strategies for Enhancing Generalizability
6.4.5 Regulatory and Legal Frameworks
6.5 Case Studies and Applications of AI in Healthcare
6.5.1 Diagnosing Diseases with AI
6.5.2 Predictive Analytics for Patient Monitoring
6.5.3 Personalized Treatment Recommendations
6.5.4 AI-Assisted Robotic Surgery
6.5.5 Drug Discovery and Development
6.5.5.1 Target Identification and Validation
6.5.5.2 Virtual Screening and Drug Design
6.5.5.3 Drug Repurposing
6.5.5.4 Predictive Toxicology and Safety Assessment
6.5.5.5 Clinical Trial Optimization
6.5.5.6 Real-Time Monitoring and Surveillance
6.5.5.7 Data Integration and Analysis
6.5.6 Virtual Assistants and Chatbots
6.6 Future Directions and Opportunities in AI for Healthcare
6.6.1 Integration of AI with Precision Medicine
6.6.2 AI-Powered Drug Discovery and Development
6.6.3 Augmented Decision Support Systems
6.6.4 Telehealth and Remote Patient Monitoring
6.6.5 Explainable AI and Ethical Considerations
6.7 Conclusion
References
7. Exploring the Advantages and Security Aspects of Digital Twin Technology in Healthcare
Srinivas Kumar Palvadi, Pradeep K. G. M. and G. Kadiravan
7.1 Introduction
7.2 Benefits
7.3 Security Considerations
7.4 Contribution in this Domain to Healthcare
7.5 Medical Device Development
7.6 Digital Twin Technology in Healthcare in Future
7.7 Continuous UI Upgrades
7.7.1 Getting Started with this Domain in Healthcare
7.7.2 Future Challenges in the Field
7.8 Conclusion
References
8. An Extensive Study of AI and Cybersecurity in Healthcare
Hemlata, Manish Rai and Utsav Krishan Murari
8.1 Introduction
8.1.1 Speculating About the Use of AI in Medical Care in the Future
8.1.2 Managing the Exchange of Information
8.1.3 Considering that Governments Function as Strategic Actors
8.1.4 Cybersecurity
8.2 Literature Review
8.3 Methodology
8.4 AI Cybersecurity’s Significance for Healthcare
8.5 Difficulties with AI Cybersecurity
8.6 Conclusion
References
9. Cloud Computing in Healthcare: Risks and Security Measures
Neha Gupta, Rashmi Agrawal and Kavita Arora
Introduction
Current State of Healthcare Industry
Cloud Computing in Healthcare
Benefits of Adopting Cloud in Healthcare
Drivers for Cloud Adoption in Healthcare
Cloud Challenges in Healthcare
Cloud Computing–Based Healthcare Services
Current Market Dynamics
Impact of Cloud Computing in Indian Healthcare Firms
Conclusion
References
10. Explainable Artificial Intelligence in Healthcare: Transparency and Trustworthiness
Sakshi and Gunjan Verma
10.1 Introduction
10.1.1 Role of XAI in AI
10.1.1.1 Explain to Justify
10.1.1.2 Explain to Control
10.1.1.3 Explain to Discover
10.1.1.4 Explain to Improve
10.1.2 Importance of Explainable Artificial Intelligence
10.1.2.1 Understanding the Need for Explainability
10.1.2.2 Benefits of XAI in Healthcare
10.1.3 Addressing the Challenges of XAI Adoption
10.1.3.1 Complexity of AI Models
10.1.3.2 Trade-Offs Between Accuracy and Interpretability
10.1.3.3 Ensuring Generalizability and Robustness
10.2 Working of XAI in Healthcare
10.2.1 Data Collection
10.3 Explorable Artificial Intelligence Techniques and Methods in Healthcare
10.3.1 Rule-Based Systems
10.3.2 Interpretable Machine Learning Models
10.3.3 Visualizations (e.g., Heatmaps)
10.3.4 Model-Agnostic Methods (e.g., LIME, SHAP)
10.4 Interpretable Deep Learning Models
10.4.1 Attention Mechanisms
10.4.2 Saliency Maps
10.4.3 Concept Activation Vectors
10.4.4 Layer-Wise Relevance Propagation
10.4.5 Rule Extraction
10.4.6 Model Visualization Techniques
10.5 Clinical Decision Support System
10.6 Explainable Clinical Natural Language Processing
10.6.1 Interpretability Techniques for Clinical Text Classification
10.6.2 Explaining Named Entity Recognition in Clinical NLP
10.6.3 Enhancing Interpretability in Medical Coding
10.7 User-Centered Design of XAI Systems
10.8 Regulatory and Legal Perspectives in XAI for Healthcare
10.8.1 Regulations
10.8.2 Legal Framework
10.8.3 Data Governance and Privacy Regulations
10.8.4 Model Transparency and Accountability
10.8.5 Algorithmic Bias and Fairness
10.8.6 Explainability and Interpretability
10.8.7 Ethical and Legal Responsibility
10.9 Ethical Considerations in Explainable Artificial Intelligence (XAI) for Healthcare
10.9.1 Bias and Fairness
10.9.2 Privacy and Informed Consent
10.9.3 Security and Protection Against Adversarial Attacks
10.10 Strategies for Promotion of Accountable Use of XAI in Healthcare
10.10.1 Explainability and Transparency
10.10.2 Human-AI Collaboration and Shared Decision Making
10.10.3 Regulatory Frameworks and Ethical Guidelines
10.10.4 Continuous Monitoring and Evaluation
Conclusion
References
11. Fuzzy Expert System to Diagnose the Heart Disease Risk Level
B. Lakshmi, K. Sarath, K. Parish Venkata Kumar, G. Praveen, B. Karthik and Y. Phani Bhushan
11.1 Introduction
11.2 Work Related
11.3 Expert Methods for Medical Diagnosis
11.4 Parameter Input
11.4.1 Cholesterol
11.4.2 Blood Pressure (BP)
11.4.3 Sugar Blood
11.4.4 Rate of Heart
11.4.5 Glucose Meter
11.4.6 Monitor Blood Pressure
11.5 System Flow
11.5.1 Input and Output of Fuzzy
11.5.2 System Workflow Based on Fuzzy
11.5.3 Data Set
11.6 Simulation and Result
11.6.1 Accuracy Level of Expert System
11.7 Conclusion
References
12. Search and Rescue–Based Sparse Auto‑Encoder for Detecting Heart Disease in IoT Healthcare Environment
Rakesh Chandrashekar, B. Gunapriya and Balasubramanian Prabhu Kavin
12.1 Introduction
12.2 Related Works
12.3 Proposed Model
12.3.1 Dataset Description
12.3.2 Pre-Processing
12.3.3 Feature Selection Using Artificial Fish Swarm Optimization (AFO)
12.3.3.1 Prey Behavior
12.3.3.2 Swarm Behavior
12.3.3.3 Follow Behavior
12.3.4 Prediction of Heart Disease Using ISAE Model
12.3.4.1 Design of the SRO Algorithm
12.4 Results and Discussion
12.4.1 An Experimental Setup Details
12.4.2 Experiment System Characteristics
12.4.3 Performance Metrics
12.5 Conclusion and Future Work
References
13. Growth Optimization–Based SBLRNN Model for Estimate Breast Cancer in IoT Healthcare Environment
Jayasheel Kumar Kalagatoori Archakam, Santosh Kumar B. and Balasubramanian Prabhu Kavin
13.1 Introduction
13.2 Related Works
13.2.1 Challenges
13.3 Proposed Model
13.3.1 Overall IoMT-Based Basis
13.3.2 Proposed Methodology
13.3.2.1 Stacked Bidirectional LSTM RNN for Disease Prediction
13.3.2.2 Growth Optimizer
13.4 Results and Discussion
13.4.1 Dataset
13.4.1.1 Wisconsin Breast Cancer Dataset
13.4.2 Model Assessment
13.5 Conclusion
References
14. Lightweight Fuzzy Logical MQTT Security System to Secure the Low Configurated Medical Device System by Communicating the IoT
Basi Reddy A., Kanegonda Ravi Chythanya, Sharada K. A. and R. Senthamil Selvan
14.1 Introduction
14.2 Methodology of FLS
14.3 Problem Identification
14.3.1 Framework
14.3.1.1 Threat Modelling
14.3.1.2 Attack Outline
14.3.1.3 Design Idea
14.4 Proposed Approach
14.5 Result with Discussion
14.5.1 Intrusion Detection System Analysis Metrics
14.5.1.1 Threat Detection Efficiency
14.5.1.2 Threat Detection Rate
14.5.1.3 Threat Detection Accuracy (TDA) Ratio
14.5.1.4 False vs. Positive Rate (FPR)
14.5.2 Communication Rate
14.5.2.1 Precision
14.5.2.2 Recall
14.5.2.3 F-Score
14.6 Conclusion
References
15. IoT-Based Secured Biomedical Device to Remote Monitoring to the Patient
Dinesh G., Jeevanarao Batakala, Yousef A. Baker El-Ebiary and N. Ashokkumar
15.1 Introduction
15.2 Internet of Things
15.3 IoMT
15.3.1 Real Application of IoT
15.3.2 Ransomware
15.3.2.1 Target and Ransomware Implications
15.3.2.2 How Ransomware Works
15.4 Biostatistical Techniques for Maintaining Security Goals
15.5 Healthcare IT System Through Biometric BioMT Approach
15.6 Conclusion
References
16. Fuzzy Interface Drug Delivery Decision-Making Algorithm
Yogendra Narayan, Mukta Sandhu, Yousef A. Baker El-Ebiary and N. Ashokkumar
16.1 Introduction
16.2 Description and Problems
16.3 Methods
16.3.1 Tree Decision
16.3.2 Fuzzy Inference System
16.3.3 Fuzzification of Decision Rules of Tree
16.3.4 FIS Decision Making
16.4 Application of Analgesia
16.4.1 Analgesia Nociception Index
16.4.2 Data Collection/Preprocessing
16.5 Result
16.5.1 FIS of Structure
16.6 Discussion
16.7 Conclusion
References
17. Implementation of Clinical Fuzzy‑Based Decision Supportive System to Monitor Renal Function
S. Dinesh Kumar, M. J. D. Ebinezer and N. Ashokkumar
17.1 Introduction
17.1.1 Expert Systems of FIS
17.1.2 Neuro Adaptive of FIS
17.1.2.1 Fuzzification Layer, First Layer
17.1.2.2 Law Layer, Second Layer
17.1.2.3 Normalization Layer, Fourth Layer
17.1.2.4 Defuzzification
17.1.2.5 The Summation Layer, or Fifth Layer
17.2 Work Related
17.3 Methods
17.3.1 MATLAB
17.4 Discussion and Results
17.5 Conclusion
References
18. Deep Learning–Based Medical Image Classification and Web Application Framework to Identify Alzheimer’s Disease
K. Parish Venkata Kumar, Piyush Charan, S. Kayalvili and M. V. B. T. Santhi
18.1 Introduction
18.2 Proposed Methodology
18.2.1 Various Techniques Used
18.3 Experiment Setup
18.4 Result
18.5 Discussion of Result
18.6 Conclusion
References
19. Using Deep Learning to Classify and Diagnose Alzheimer’s Disease
A. V. Sriharsha
19.1 Introduction
19.2 Biomarkers and Detection of Alzheimer’s Disease
19.2.1 AD Biomarkers
19.2.2 Data Preprocessing
19.2.3 Management of Data
19.2.4 Patch Based
19.3 Methods
19.3.1 The E2AD2C Framework
19.3.2 Data Normalization
19.3.3 Methods and Technique
19.4 Model Evaluation and Methods
19.4.1 Checking the Web Services
19.4.2 Other Fuzzy Systems of Diagnosis of Diseases
19.5 Conclusion
References
20. Developing a Soft Computing Fuzzy Interface System for Peptic Ulcer Diagnosis
B. Lakshmi, K. Parish Venkata Kumar and N. Ashokkumar
20.1 Introduction
20.2 Methodology
20.2.1 Animals
20.2.2 Method Chemical of Gastric Ulcer
20.2.3 Index Measurement of Ulcer
20.2.4 Data Sets
20.2.5 Fuzzy Expert System
20.3 Results
20.3.1 Variables of Input and Output
20.3.2 Methods
20.3.3 EOC Analysis
20.3.4 Other Fuzzy Expert Systems for Disease Diagnosis
20.4 Conclusion
References
21. Digital Twin Technology in Healthcare: Benefits and Security Considerations
Priyanka Tyagi and Kajol Mittal
Introduction
Conclusion
References
22. Combating Cyber Threats Including Wormhole Attacks in Healthcare Cyber-Physical Systems: Advanced Prevention and Mitigation Techniques
Pramod Singh Rathore and Mrinal Kanti Sarkar
22.1 Introduction to Cybersecurity in Healthcare Cyber-Physical Systems
22.2 Understanding Cyber Threats in Healthcare
22.2.1 Types of Cyber Threats in Healthcare Systems
22.2.2 Special Focus on Wormhole Attacks
22.2.3 Case Studies: Recent Cyberattacks in Healthcare
22.3 Vulnerabilities in Healthcare Cyber-Physical Systems
22.3.1 Identifying Common Vulnerabilities
22.3.2 Impact of Wormhole Attacks on Healthcare Systems
22.3.3 Assessing Risks in Connected Medical Devices
22.4 Advanced Prevention Techniques
22.4.1 Implementing Robust Encryption Protocols
22.4.2 Role of Firewalls and Intrusion Detection Systems
22.4.3 Preventive Measures for Wormhole Attacks
22.5 Mitigation Strategies for Cyber Threats
22.5.1 Developing an Effective Incident Response Plan
22.5.2 Strategies for Containing and Mitigating Wormhole Attacks
22.5.3 Disaster Recovery and Business Continuity Planning
22.6 Emerging Technologies and Future Trends
22.6.1 The Role of Artificial Intelligence in Cybersecurity
22.6.2 Blockchain for Secure Healthcare Data Management
22.6.3 Future Challenges and Opportunities in Healthcare Cybersecurity
22.7 Training and Awareness Programs
22.7.1 Educating Healthcare Staff on Cybersecurity Best Practices
22.7.2 Training Programs for Wormhole Attack Prevention
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page