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Submit a ProposalCyber Shields Using Artificial Intelligence
One Line Description
In an era where traditional firewalls and antivirus software no longer stand a chance against sophisticated cyberattacks, this book provides the essential roadmap you need to secure next-generation cloud, IoT, and decentralized systems against catastrophic data breaches and ransomware.
Description
Over the past few decades, traditional cybersecurity measures have proven insufficient against the ever-growing sophistication of cyberattacks. As technology advances, particularly with the proliferation of cloud computing, the Internet of Things, and decentralized applications, cybersecurity professionals are faced with new and more challenging security risks, ranging from data breaches to cyber-espionage and ransomware attacks. This book delves into the transformative impact of artificial intelligence and deep learning on contemporary cybersecurity practices. It provides a detailed exploration of how cutting-edge AI and deep learning methodologies are being leveraged to secure digital ecosystems, mitigate emerging threats, and strengthen cryptographic protocols. By integrating traditional security approaches with advanced machine learning and neural networks, the book offers readers a robust understanding of the next-generation tools and frameworks that are shaping the cybersecurity landscape. In addition to theoretical foundations, the book features practical case studies and real-world applications. Readers will gain insights into the use of AI for securing cloud environments, IoT networks, and mobile platforms. Emerging challenges such as securing AI models against adversarial attacks and ethical considerations in AI-driven cybersecurity are also discussed. With contributions from experts in academia and industry, the book offers a balanced perspective, making it a valuable resource for researchers, practitioners, and students at advanced undergraduate, graduate, and doctoral levels.
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Over the past few decades, traditional cybersecurity measures have proven insufficient against the ever-growing sophistication of cyberattacks. As technology advances, particularly with the proliferation of cloud computing, the Internet of Things, and decentralized applications, cybersecurity professionals are faced with new and more challenging security risks, ranging from data breaches to cyber-espionage and ransomware attacks. This book delves into the transformative impact of artificial intelligence and deep learning on contemporary cybersecurity practices. It provides a detailed exploration of how cutting-edge AI and deep learning methodologies are being leveraged to secure digital ecosystems, mitigate emerging threats, and strengthen cryptographic protocols. By integrating traditional security approaches with advanced machine learning and neural networks, the book offers readers a robust understanding of the next-generation tools and frameworks that are shaping the cybersecurity landscape. In addition to theoretical foundations, the book features practical case studies and real-world applications. Readers will gain insights into the use of AI for securing cloud environments, IoT networks, and mobile platforms. Emerging challenges such as securing AI models against adversarial attacks and ethical considerations in AI-driven cybersecurity are also discussed. With contributions from experts in academia and industry, the book offers a balanced perspective, making it a valuable resource for researchers, practitioners, and students at advanced undergraduate, graduate, and doctoral levels.
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Author / Editor Details
Shubham Mahajan, PhD is a Post Doctoral Reseacher in the Department of Applied Data Science at Noroff Univeristy College in Kristiansand, Norway. He holds eleven Indian patents, one Australian and one German patent and has published more than 70 articles in peer-reviewed journals and conferences. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.
Shubham Mahajan, PhD is a Post Doctoral Reseacher in the Department of Applied Data Science at Noroff Univeristy College in Kristiansand, Norway. He holds eleven Indian patents, one Australian and one German patent and has published more than 70 articles in peer-reviewed journals and conferences. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.
Kamal Upreti, PhD is an Associate Professor in the Department of Computer Science at Christ University), Ghaziabad, India. He has published more than 50 patents, 32 magazine issues, 110 research papers in journals and international conferences, and 45 books. His areas of interest include modern physics, data analytics, cyber security, machine learning, healthcare, and embedded systems.
Rashmi Agrawal, PhD is a Professor and Associate Dean at the School of Computer Applications at the Manav Rahna International Institute of Research and Studies, Faridabad, India with more than 23 years of experience. She has published more than 100 research papers in esteemed national and international journals and conferences and authored and co-authored books with distinguished publishers. Her research focuses on machine learning, classification, and pattern recognintion.
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Table of Contents
Preface
1. Emerging Trends in Artificial Intelligence for Cybersecurity
Utpal Ghosh and Shrabanti Kundu
1.1 Introduction
1.1.1 Historical Context and Motivation for AI in Cybersecurity
1.1.2 Understanding the Cyber Threat Landscape
1.1.3 Role of AI in Cybersecurity
1.1.4 Key AI Techniques in Cybersecurity
1.1.4.1 Machine Learning
1.1.4.2 Deep Learning
1.1.4.3 Reinforcement Learning
1.1.4.4 Transfer Learning
1.1.4.5 Zero-Shot Learning
1.1.5 Challenges and Limitations
1.1.6 Future Directions
1.2 Literature Review
1.2.1 Early Applications of AI in Cybersecurity
1.2.2 Evolution of ML Techniques in Cybersecurity
1.2.3 Emergence of DL in Cybersecurity
1.2.4 Rise of RL in Cybersecurity
1.2.5 Advent of TL and ZSL
1.2.6 Current Challenges and Research Gaps
1.2.6.1 Interpretability and Trust
1.2.6.2 Adversarial ML
1.2.6.3 Data Imbalance and Scarcity
1.2.6.4 Dynamic Threat
1.2.6.5 Ethical and Privacy
1.3 Methodology
1.3.1 Experimental Design
1.3.1.1 Model Selection and Implementation
1.3.1.2 Dataset Preprocessing and Feature Engineering
1.3.1.3 Training, Validation, and Testing
1.3.2 AI Models Utilized
1.3.2.1 ML Models
1.3.2.2 DL Models
1.3.2.3 RL Models
1.3.2.4 TL Models
1.3.2.5 Models for ZSL
1.3.3 Datasets
1.3.4 Evaluation Metrics
1.3.5 Cross-Validation and Hyperparameter Tuning
1.3.6 Ethical Considerations
1.4 Results and Comparative Analysis
1.4.1 Performance on Intrusion Detection Tasks
1.4.1.1 NSL-KDD Dataset
1.4.1.2 CICIDS 2017 Dataset
1.4.2 Performance on Malware Detection Tasks
1.4.2.1 EMBER 2018 Dataset
1.4.3 ZSL Results
1.4.4 Summary of Comparative Analysis
1.5 Discussion, Challenges, and Research Gap
1.5.1 Discussion
1.5.2 Challenges and Research Gaps
1.6 Conclusions
References
2. Deep Learning for Cybersecurity
Himani Tyagi, Aditya Dayal Tyagi and Swati Sah
2.1 Introduction
2.1.1 Overview of the Cybersecurity Landscape
2.1.2 Need for Automation and Intelligence in Threat Detection
2.1.3 Why DL for Cybersecurity?
2.2 Fundamentals of DL
2.2.1 Brief Introduction to DL
2.2.2 Common Architectures: CNN, RNN, LSTM, GANs, and Transformers
2.2.3 Convolutional Neural Networks
2.2.4 Recurrent Neural Networks
2.2.5 Long Short-Term Memory Networks
2.2.6 Generative Adversarial Networks
2.2.7 Transformers
2.2.8 Relevance of DL Characteristics to Cybersecurity Tasks
2.2.8.1 Automatic Feature Extraction
2.2.8.2 Scalability to Big Data
2.2.8.3 Adaptability and Generalization
2.2.8.4 Sequential and Temporal Pattern Recognition
2.2.8.5 Robustness to Noisy Data
2.2.8.6 Adversarial Learning and Simulation
2.2.8.7 Real-Time Detection and Decision-Making
2.3 DL Applications in Cybersecurity
2.3.1 Malware Detection
2.3.1.1 Static Analysis versus Dynamic Analysis
2.3.1.2 DL Techniques for Malware Classification
2.3.2 Intrusion Detection Systems
2.3.3 Phishing Detection
2.3.4 Spam and Botnet Detection
2.3.5 CTI Automation
2.3.6 Adversarial Attack Detection
2.3.7 User and Entity Behavior Analytics
2.4 DL Techniques and Architectures in Cybersecurity
2.4.1 CNN for Image-Based Malware
2.4.2 RNN and LSTM for Sequential Logs
2.4.3 Autoencoders for Anomaly Detection
2.4.4 GANs for Attack Simulation and Detection
2.4.5 Transformer Models for Cybersecurity Text Data
2.5 Challenges and Limitations
2.5.1 Adversarial ML
2.5.2 Interpretability and Explainability Issues
2.5.3 Data Scarcity and Data Imbalance
2.5.4 Computational Cost and Model Deployment Hurdles
2.5.5 Privacy Concerns in Cybersecurity Datasets
2.6 Emerging Trends
2.6.1 FL for Distributed Security Systems
2.6.2 SSL and Its Potential
2.6.3 XAI in Cybersecurity
2.6.4 ZTA with AI Assistance
2.6.5 Real-Time Threat Detection Using Edge AI
2.7 Case Studies
2.7.1 Real-World Applications of DL in Cybersecurity
2.7.2 Success Stories and Lessons Learned
2.7.3 Analysis of Some Well-Known Attacks and DL Responses
2.7.3.1 WannaCry Ransomware (2017)
2.7.3.2 SolarWinds Supply Chain Attack (2020)
2.7.3.3 Twitter Bitcoin Scam (2020)
2.7.4 Bridging the Gap between Research and Practice
2.7.5 Conclusion of Case Studies
2.8 Future Directions
2.8.1 Building Resilient AI Models against Adversarial Attacks
2.8.2 Human–AI Collaboration for Cybersecurity
2.8.3 Regulatory Frameworks and Ethical Considerations
2.9 Conclusion
2.9.1 Summary of Key Points
2.9.2 The Evolving Role of DL in Securing Digital Environments
Bibliography
3. Cloud and IoT Security with AI
Amandeep Kaur, Ramandeep Sandhu, Indu Rani, Gaganpreet Kaur and Deepika Ghai
3.1 Introduction
3.2 Integration of AI in People Analytics
3.2.1 Defining People Analytics
3.2.2 AI Technologies in People Analytics
3.2.3 Case Studies
3.2.4 Challenges and Ethical Considerations
3.3 AI-Driven Marketing Strategies
3.3.1 The Evolution of Marketing in the Digital Era
3.3.2 Utilizing Consumer Data with AI
3.3.3 Impact on Sales Performance
3.3.4 Ethical Marketing Practices
3.4 AI in Infrastructure Finance
3.4.1 Overview of Infrastructure Finance
3.4.2 AI-Assisted Risk Evaluation
3.4.3 Data-Driven Decision-Making in Finance
3.4.4 Challenges in Implementation
3.5 Security Challenges in Cloud and IoT Environments
3.5.1 Overview of Security Risks
3.5.2 AI-Driven Security Solutions
3.5.3 Data Intrusion and Authentication Issues
3.5.4 Privacy Concerns and Compliance
3.6 Integrating AI Security Mechanisms
3.6.1 Framework for AI Security in Business Operations
3.6.2 Best Practices for Implementation
3.6.3 Future Trends in AI Security
3.7 Literature Review and Current Trends
3.7.1 Overview of Existing Research
3.7.2 Modern Business Trends
3.7.3 Security Obstacles and Prevention Methods
3.8 Conclusion and Recommendations
3.8.1 Summary of Key Insights
3.8.2 Implications for Businesses and Policymakers
3.8.3 Future Research Directions
References
4. Reinforcement and Deep Learning Approaches to Dynamic
Cache Optimization
Patel Smit Vasant Kumar, Kruti Dataram, Uma Shankar, Sonam Nagpal and Himanshu Amritlal Patel
4.1 Introduction
4.1.1 Traditional Cache Management Techniques and Their Limitations
4.2 Fundamentals of Cache Management
4.2.1 Cache Memory Architecture (L1, L2, L3)
4.2.2 Caching Policies (LRU, LFU, FIFO)
4.2.3 Key Performance Metrics (Hit Ratio, Latency, Throughput)
4.2.4 Challenges in Cache Management in Modern Systems (e.g., Mobile, Cloud, Edge Devices)
4.3 Role of AI in Systems Optimization
4.3.1 AI Techniques Overview
4.4 ML Approaches to Cache Management
4.4.1 Predictive Modeling for Access Patterns
4.4.2 Feature Selection and Importance (e.g., Memory Address Sequences, Access Time)
4.4.3 Evaluation Framework and Datasets (SPEC Benchmarks, Real-World Traces)
4.5 RL for Adaptive Cache Policies
4.5.1 Q-Learning and DQNs for Cache Decisions
4.5.2 Reward Design: Hit/Miss Tradeoffs and Energy Efficiency
4.5.3 Simulation Environment and State Representation
4.5.4 Case Studies and Implementations
4.6 DL in Cache Prefetching and Replacement
4.6.1 Sequence Modeling with Recurrent Neural Networks/Long Short-Term Memorys for Prefetching
4.6.2 Autoencoders for Cache Content Compression
4.6.3 Transformers in Cache Prediction Tasks
4.7 AI in Edge, Cloud, and Multicore Cache Environments
4.7.1 Cache Challenges in Distributed and Heterogeneous Systems
4.7.2 Federated Learning for Cache Management in IoT/Edge
4.7.3 AI in Multicore Cache Coherency Management
References
5. ARKANA: Extracting Knowledge Using Deep Learning for Intrusion Detection From Data Streams
Aravindan V., Rajkanwar Singh, Sanket Mishra and Sandipan Maiti
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.3.1 Data Ingestion and Pipeline
5.3.2 Data Resampling
5.3.3 Feature Selection
5.3.4 Classifiers
5.3.5 Ranking Algorithm
5.3.6 Serving
5.4 Evaluation Metrics
5.5 Results and Discussion
5.5.1 CICEVSE Dataset
5.5.2 ToN_IoT Dataset
5.6 Conclusion
References
6. Blockchain-Enabled Forensic Analysis in Digital Forensics in Cryptography
Preety Shoran, Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Mayur Dattatray Mali and Gauri Mayur Mali
6.1 Introduction to Blockchain-Enabled Forensic Analysis
6.2 Fundamentals of Blockchain Technology in Cryptography
6.3 Challenges in Traditional Digital Forensics in Cryptography
6.4 Integration of Blockchain in Digital Forensics Cryptography
6.5 Blockchain-Based Evidence Management in Cryptography
6.6 Blockchain-Driven Chain-of-Custody Verification
6.7 Enhanced Data Integrity and Authenticity
6.8 Smart Contracts for Automated Forensic Processes
6.9 Decentralized Forensic Investigation Networks
6.10 Case Studies and Practical Implementations
6.11 Challenges and Future Directions
6.12 Conclusion
References
7. Blockchain in Cyber Defense: Strengths, Weaknesses, and Strategic Implications
Pawan Kumar, Sukhvir Singh, Deepika, Ruchi, Jyoti Parashar and Virendra Singh Kushwah
7.1 Introduction
7.1.1 Background
7.1.2 Role of Blockchain in Cybersecurity
7.1.3 Research Objectives
7.1.4 Structure of the Paper
7.2 Fundamentals of Blockchain Technology
7.2.1 Blockchain Architecture
7.2.2 Types of Blockchain
7.2.3 Security Features of Blockchain
7.3 Cybersecurity Risks and Threats
7.3.1 Traditional Cybersecurity Challenges
7.3.2 Blockchain-Specific Security Risks
7.4 Blockchain-Based Cybersecurity Solutions
7.4.1 Enhancing Data Integrity
7.4.2 Identity and Access Management
7.4.3 Preventing Cyberattacks
7.4.4 Smart Contracts for Automated Security
7.5 Comparative Analysis of Blockchain Security Approaches
7.5.1 Overview of Major Blockchain Platforms
7.5.2 Security Strengths and Limitations
7.5.3 Case Studies and Technical Evaluations
7.5.4 Comparative Metrics
7.6 Challenges and Future Research Directions
7.6.1 Regulatory and Compliance Challenges
7.6.2 Interoperability and Integration
7.6.3 Evolving Consensus Mechanisms and Security Frameworks
7.6.4 Future Trends and Research Opportunities
7.7 Conclusion
References
8. Self-Sovereign Identity and Blockchain: Enhancing Privacy and Security in Academic Credential Systems
Omar S. Saleh, Osman Ghazali, Norbik Bashah Idris and Muhammad Ehsan Rana
8.1 Introduction
8.1.1 Background and Context
8.1.2 Motivation for the Chapter
8.1.3 Chapter Objectives
8.2 Theoretical Foundations
8.2.1 Self-Sovereign Identity
8.2.2 Blockchain Technology
8.2.3 The Intersection of SSI and Blockchain
8.3 Current Challenges in Academic Credential Systems
8.3.1 Privacy Concerns
8.3.2 Security Issues
8.3.3 Interoperability and Portability
8.4 SSI and Blockchain in Academic Credential Systems
8.4.1 How SSI and Blockchain Address Current Challenges
8.4.2 Use Cases and Examples
8.4.3 Technical Architecture
8.5 Benefits and Advantages
8.5.1 Enhanced Privacy
8.5.2 Improved Security
8.5.3 Interoperability and Global Recognition
8.5.4 Limitations and Adoption Barriers
8.6 Future Directions
8.6.1 Emerging Trends
8.6.2 Potential Applications beyond Academia
8.6.3 Research Opportunities
8.7 Conclusion
8.7.1 Summary of Key Points
8.7.2 Final Thoughts
Bibliography
9. Blockchain and Federated Learning in IoV: Real-World
Insights into Secure and Decentralized Systems
Ramanjeet Singh, Amandeep Kaur, Divneet Singh Kapoor, Kiran Jot Singh and Khushal Thakur
9.1 Introduction
9.1.1 Introduction of Intelligent Transportation Systems and IoV
9.1.1.1 Intelligent Transportation Systems
9.1.1.2 The IoV
9.1.2 Security, Privacy, Latency, and Data Management Issues in IoV
9.1.2.1 Security Issues in IoV
9.1.2.2 Privacy Concerns
9.1.2.3 Latency Challenges in IoV
9.1.2.4 Data Management in IoV
9.1.3 Blockchain and Federated Learning in IoV
9.1.3.1 The Role of Blockchain in Secure and Decentralized IoV
9.1.3.2 FL for Privacy-Preserving Intelligent IoV Applications
9.2 Fundamentals of Blockchain and FL in IoV
9.2.1 Blockchain in IoV
9.2.1.1 Key Concepts: Decentralization, Immutability, and Smart Contracts
9.2.1.2 Consensus Mechanisms for IoV
9.2.1.3 Security Enhancements Using Blockchain
9.2.2 FL in IoV
9.2.2.1 Concept and Working Mechanism of FL
9.2.2.2 Privacy-Preserving ML Privacy
9.2.2.3 Training Edge or Distributed Model
9.3 Case Studies on Blockchain Applications in IoV
9.3.1 Case Study 1: Secured and Transparent Vehicle Data Sharing
9.3.1.1 Problem Statement: Centralized Data Control and Trust Issues
9.3.1.2 Blockchain Solution: Decentralized Data Exchange and Smart Contracts
9.3.1.3 Implementations and Results
9.3.2 Case Study 2: Fraud Prevention in Vehicular Insurance
9.3.2.1 Problem Statement: Insurance Claim Fraud and Data Manipulation
9.3.2.2 Enabling Transparent Transactions Using Blockchain-Based Smart Contracts
9.3.2.3 Real-World Deployment Scenarios
9.3.3 Case Study 3: Blockchain in Toll Collection and Road Pricing
9.3.3.1 Problems with Traditional Tolling System
9.3.3.2 Automated Toll Collection and Blockchain-Based Micropayments
9.3.3.3 Performance Analysis
9.4 Case Studies on FL in IoV
9.4.1 Case 4: FL for Traffic Prediction and Optimization
9.4.1.1 Necessity of Distributed Learning in Reactive Traffic Control
9.4.1.2 Model Training Using FL across IoV Nodes
9.4.1.3 Comparing the Statistical Performance
9.4.2 Case Study 5: Anomaly Detection in Autonomous Vehicles
9.4.2.1 Fault and Attack Detection in Autonomous Cars with Privacy Preservation
9.4.2.2 FL-Based Cyberattack Detection Model
9.4.2.3 Comparative Analysis of the Anomaly Detection Methods
9.4.3 Case Study 6: Driver Personalized Assistance Systems
9.4.3.1 FL-Based Training of Models While Protecting User Privacy
9.4.3.2 FL-Powered ADAS Performance Evaluation
9.5 Comparison and Analysis of Blockchain with FL in IoV
9.5.1 Strengths and Limitations of Both Approaches
9.5.1.1 Advantages of Blockchain in IoV
9.5.1.2 Drawbacks of Blockchain in IoV
9.5.1.3 Strengths of FL for IoV
9.5.1.4 Limitations of FL in IoV
9.5.2 Possible Synergies between Blockchain and FL
9.5.3 Performance Metrics Comparison
9.6 Challenges and Future Directions
9.6.1 Scalability Challenge of Blockchain and FL in Large-Scale IoV
9.6.2 Security and Privacy Issues
9.6.3 Integrating 5G, Edge Computing, and AI/ML Models
9.6.4 Open Research Challenges
9.7 Concluding Remarks
9.7.1 Summary of Key Insights from Case Studies
9.7.2 Conclusion on Blockchain and FL in IoV
9.7.3 Future Trends and Opportunities
References
10. Securing Decentralized Applications: A Layered Analysis
of Vulnerabilities and Countermeasures in Ethereum dApps
Safia Farooqui, Sonali Gaur, Eshwari Girish Kulkarni, Neha Sharma, Chandan Prasad and Prakash Divakaran
10.1 Introduction
10.1.1 BC-Based Application Layers
10.1.2 Front-End Application Part
10.2 BC Structure Vulnerabilities
10.2.1 BC Forks
10.2.2 Taxonomy of Vulnerabilities in dApps
10.2.2.1 Classification by Source Layer
10.2.2.2 Classification by Impact Severity
10.3 Notorious dApp Exploits and Real-World Case Studies
10.4 Front-End and API Security Risks
10.4.1 Insecure Web3 Provider Injection
10.4.2 Phishing dApps
10.4.3 Insecure HTTP Calls to APIs/Oracles
10.4.4 Session Hijacking and Storage Leaks in MetaMask-Compatible Apps
10.5 Attacks on Oracles and Cross-Chain Bridges
10.5.1 Oracle Price Manipulation
10.5.2 Flash Loan Attacks
10.5.3 Insecure Bridges
10.6 Best Practices for Secure dApp Development
10.6.1 Following OpenZeppelin Standards
10.6.2 Unit Testing with Truffle/Hardhat
10.6.3 User Education and Awareness: The First Line of Defense
10.7 Future Threats and Evolving Attack Vectors
10.7.1 Cryptographic Techniques (e.g., zk-SNARKs, ECDSA, Threshold Signatures) Where Applicable
10.8 Token Standards and Security Comparison
10.9 Future Ethereum Protocol Changes and NFT dApp Security
10.10 Role of End-User Behavior in dApp Vulnerabilities
References
11. Surveying Modern Coverless Steganography: Stable Diffusion and Deep Learning Applications in Cybersecurity
Shaurya Chattopadhyay, Ayan Ghosh, Ayantik Rayand Pabak Indu
11.1 Introduction
11.2 Literature Review
11.3 Conclusion
References
12. Crypto-Genome Deciphering Encryption with Genetic Algorithms in Cyber Defense
G. V. Radhakrishnan, Safia Farooqui, Uma Shankar, Sonam Nagpal, Akhilesh Tiwari and Shitiz Upreti
12.1 Introduction
12.2 Related Works
12.3 Materials and Methods
12.3.1 Problem Statement
12.3.2 Proposed Work
12.3.3 Methodology
12.3.4 Proposed Algorithm
12.3.5 Initialization
12.3.6 Measurement of Physical Fitness
12.4 Experimental Configuration
12.4.1 Parameters Used for Experimentation
12.4.2 Parameters Used for Testing the Experiment
12.5 Empirical Results and Examination
12.6 Conclusion
12.7 Future Work
References
13. Decentralized Finance and Traditional Banking: Transforming Financial Inclusion for Urban Women
Subhagi Saxena and Shivani Chaudhry
13.1 Introduction
13.2 Literature Review
13.2.1 Traditional Banking
13.2.2 AI-Driven DeFi and Blockchain Technology
13.2.3 Blockchain Technology
13.2.4 Financial Inclusion
13.2.5 Women in India
13.3 Methodology
13.3.1 Research Design
13.3.2 Sampling
13.3.3 Variables
13.4 Findings
13.5 Advantages of DeFi with Traditional Banking
13.6 Challenges in the Incorporation of DeFi
13.7 Regulations for DeFi in India
13.8 Policy Implications
13.9 Conclusion
References
14. Overcoming Barriers to AI and Telehealth Adoption: Strategies for Sustainable Integration in Healthcare
Pratibha Garg, Neha Gupta, Nishant Kumar, Shitiz Upreti and Vijayalaxmi Rajendran
14.1 Introduction
14.2 Key Barriers to AI and Telehealth Adoption
14.2.1 Technological Barriers
14.2.1.1 Data Interoperability Challenges
14.2.1.2 System Integration Challenges
14.2.1.3 Scalability of AI and Telehealth Technologies
14.2.1.4 Digital Divide and Unequal Access
14.2.1.5 Security and Privacy Concerns
14.2.2 Human and Cultural Barriers
14.2.3 Ethical and Legal Barriers
14.2.4 Regulatory and Policy Barriers
14.2.5 Economic and Infrastructure Barriers
14.3 Strategies for Overcoming Adoption Barriers
14.3.1 Policy and Regulatory Reforms
14.3.2 Investment in Infrastructure
14.3.3 Workforce Development and Capacity Building
14.3.4 Inclusive and Ethical Design
14.3.5 Building Stronger Governance and Leadership
14.3.6 Public Awareness and Patient Empowerment
14.3.7 Cross-Sector Collaboration
14.4 Case Studies of Successful Implementation
14.4.1 United Kingdom’s National Health Service Digital Transformation
14.4.2 Estonia’s Digital Health System: e-Health and Telemedicine
14.4.3 India’s National Telemedicine Service: eSanjeevani
14.4.4 Sweden’s AI-Driven Healthcare: Region Västra Götaland
14.4.5 Rwanda’s Telehealth Initiative: Rural Care through Digital Health
14.5 Conclusions, Implications, and Future Directions
14.5.1 Conclusions
14.5.2 Implications
14.5.3 Future Directions
References
15. MindLift: A Mental Health Revolution Using AI
Shanky Goyal, Rubal Jeet, Ashima Shahi, Gaurav and Anmol Joshi
15.1 Introduction
15.2 Problem Statement
15.2.1 Current Mental Health Challenges
15.2.1.1 Academic Pressure
15.2.1.2 Social Challenges
15.2.1.3 Emotional Struggles
15.2.1.4 Stigma and Fear
15.2.2 Limitations of Existing Solutions
15.2.2.1 Limited Accessibility
15.2.2.2 Dependence on Self-Reporting
15.2.2.3 Lack of Personalization
15.2.2.4 Limited Emotional Analysis
15.2.2.5 Inconsistent Follow-Up and Support
15.2.2.6 Social Stigma and Hesitation
15.2.2.7 Data Privacy Concerns
15.2.2.8 MindLift’s Approach to Overcoming these Limitations
15.2.3 Why MindLift is Necessary
15.2.3.1 Proactive Identification of Mental Health Issues
15.2.3.2 Personalized Support for Diverse Needs
15.2.3.3 Accessible Anytime, Anywhere
15.2.3.4 Real-Time Emotional Analysis
15.2.3.5 Reducing Dependence on Self-Reporting
15.2.3.6 Anonymity and Safe Space for Expression
15.2.3.7 Seamless Integration with Educational Environments
15.2.3.8 Data Security and Privacy
15.2.3.9 Consistent Monitoring and Follow-Up
15.3 MindLift Features and Functionality
15.3.1 Sentiment Analysis
15.3.1.1 Text Sentiment Analysis
15.3.1.2 Audio Sentiment Analysis
15.3.1.3 Facial Expression Analysis
15.3.1.4 Multimodal Emotion Analysis
15.3.2 Conversational Chatbot
15.3.2.1 Real-Time Emotional Support
15.3.2.2 Coping Strategies
15.3.2.3 Review of Resources
15.3.2.4 Continuous Learning
15.3.2.5 Emergency Assistance
15.3.3 Mood Tracking and Data Insights Tool
15.3.3.1 Daily Mood Logs
15.3.3.2 Behavioral Analysis
15.3.3.3 Personalized Reports
15.3.3.4 Predictive Insights
15.3.4 Privacy and Security
15.3.4.1 End-to-End Encryption
15.3.4.2 Anonymous Mode
15.3.4.3 Regulatory Compliance
15.3.5 Mobile Integration
15.3.5.1 Cross-Platform Access
15.3.5.2 Offline Support
15.3.5.3 Real-Time Notifications
15.3.5.4 Seamless Syncing
15.4 Proposed Solution
15.4.1 Data Processing Layer
15.4.1.1 Data Sources
15.4.1.2 Data Preprocessing
15.4.2 AI Models
15.4.2.1 Natural Language Processing Model
15.4.2.2 Speech Analysis Model
15.4.2.3 Computer Vision Model
15.4.3 Recommendation Engine
15.4.3.1 Coping Strategy Suggestions
15.4.3.2 Personalized Resource Recommendations
15.4.3.3 Continuous Learning
15.4.4 User Interface
15.4.4.1 Conversational Chatbot
15.4.4.2 Mood Tracking Dashboard
15.4.4.3 Data Insights and Reports
15.5 Implementation Strategy
15.5.1 Phase 1: Data Collection
15.5.2 Phase 2: Training and Testing the Models
15.5.3 Phase 3: Mobile App Development
15.5.4 Phase 4: Pilot Testing
15.5.5 Phase 5: Deployment
15.6 Evaluation and Results
15.6.1 Accuracy
15.6.2 User Satisfaction
15.6.3 Engagement
15.6.4 Impact Assessment
15.7 Future Scope
15.7.1 Expanded Emotion Detection
15.7.1.1 Advanced AI Algorithms
15.7.1.2 Multimodal Emotion Recognition
15.7.1.3 Contextual Analysis
15.7.2 Personalized Therapy Suggestions
15.7.2.1 Designing Collaborative Therapy
15.7.2.2 Customized Coping Plans
15.7.2.3 Therapist Connectivity
15.7.2.4 AI-Assisted Therapy Companion
15.7.3 Wearable Integration
15.7.4 Enhanced Data Insights
15.7.5 Community and Peer Support
15.7.6 Global Expansion and Multilingual Support
15.8 Conclusion
References
16. Bibliometric Analysis of Healthcare Metaverse: Trends
and Future Research Directions
Nishant Kumar, Divya Mohan, Neha, Shitiz Upreti and Kamini S. Bhardwaj
16.1 Introduction
16.2 Literature Review
16.3 Methods
16.3.1 Research Design
16.3.2 Data Source: Scopus Database
16.3.3 Search Strategy
16.3.4 Inclusion and Exclusion Criteria
16.3.5 Tools for Bibliometric Analysis
16.3.6 Analytical Framework
16.4 Findings and Discussion
16.4.1 Number of Publications Over Time
16.4.2 Number of Source Citations Over Time
16.4.3 Institutional Contribution Over Time
16.4.4 Countries’ Contribution Over Time
16.4.5 Author’s Contribution in the Field
16.4.6 Highly Cited Publication
16.4.7 Source Document per Year
16.4.8 Most Frequent Keyword
16.4.9 Cocitation Analysis
16.4.10 Coauthorship Analysis
16.4.11 Co-Occurrence Analysis of Keywords
16.4.12 Bibliographic Coupling
16.5 Emerging Themes
16.6 Conclusion
16.6.1 Implications
16.6.2 Limitations and Future Research
References
17. Blockchain in Intelligent Manufacturing Systems
Pallab Banerjee, Ahmad Faraz, Mohit Kumar, Ashwani Kumar and Uma Shankar
17.1 Introduction
17.1.1 IMSs’ History
17.1.2 Blockchain’s Importance in Manufacturing
17.1.3 The Chapter’s Goals
17.2 Literature Review
17.2.1 Supply Chain Management with Blockchain
17.2.2 Blockchain in Industry 4.0 and Smart Factories
17.2.3 Using Blockchain for Predictive Maintenance and Quality Assurance
17.2.4 Difficulties and Restrictions
17.2.5 The Literature’s Gaps
17.3 Blockchain’s Technical Underpinnings in Manufacturing
17.3.1 Fundamental Blockchain Technology Elements
17.3.2 Manufacturing Blockchain Architectures
17.3.3 Industry 4.0 Technology Integration
17.3.4 Manufacturing Smart Contracts
17.3.5 Data Integrity and Security
17.3.6 Performance and Scalability Considerations
17.4 Blockchain Applications for Intelligent Manufacturing
17.4.1 Traceability of the Supply Chain
17.4.2 Assurance and Quality Control
17.4.3 Maintenance Prediction
17.4.4 Teamwork in Manufacturing
17.4.5 Protection of Intellectual Property
17.4.6 New Uses
17.5 Case Studies
17.5.1 Case Study 1: Blockchain in Ford and IBM’s Automotive Supply Chain
17.5.2 Case Study 2: Merck Pharmaceutical Manufacturing Quality Control
17.5.3 Case Study 3: Boeing’s Aerospace Predictive Maintenance
17.5.4 Case Study 4: ManuChain’s Collaborative Manufacturing in SMEs
17.5.5 Knowledge Acquired
17.6 Challenges and Limitations
17.6.1 Problems with Scalability
17.6.2 Compatibility with Current Systems
17.6.3 Energy Consumption
17.6.4 Adoption Expenses and Difficulties
17.6.5 Legal and Regulatory Obstacles
17.6.6 Collaboration among Stakeholders and Trust
17.6.7 Techniques for Overcoming Obstacles
17.7 Future Directions and Recommendations
17.7.1 Using Emerging Technologies in Integration
17.7.2 Interoperability and Standardization
17.7.3 Energy Efficiency and Scalability
17.7.4 Frameworks for Policies and Regulations
17.7.5 Adoption and Workforce Development
17.7.6 Prospects for Research
17.7.7 Implementation Suggestions
17.8 Conclusion
References
Index
Back to Top
Preface
1. Emerging Trends in Artificial Intelligence for Cybersecurity
Utpal Ghosh and Shrabanti Kundu
1.1 Introduction
1.1.1 Historical Context and Motivation for AI in Cybersecurity
1.1.2 Understanding the Cyber Threat Landscape
1.1.3 Role of AI in Cybersecurity
1.1.4 Key AI Techniques in Cybersecurity
1.1.4.1 Machine Learning
1.1.4.2 Deep Learning
1.1.4.3 Reinforcement Learning
1.1.4.4 Transfer Learning
1.1.4.5 Zero-Shot Learning
1.1.5 Challenges and Limitations
1.1.6 Future Directions
1.2 Literature Review
1.2.1 Early Applications of AI in Cybersecurity
1.2.2 Evolution of ML Techniques in Cybersecurity
1.2.3 Emergence of DL in Cybersecurity
1.2.4 Rise of RL in Cybersecurity
1.2.5 Advent of TL and ZSL
1.2.6 Current Challenges and Research Gaps
1.2.6.1 Interpretability and Trust
1.2.6.2 Adversarial ML
1.2.6.3 Data Imbalance and Scarcity
1.2.6.4 Dynamic Threat
1.2.6.5 Ethical and Privacy
1.3 Methodology
1.3.1 Experimental Design
1.3.1.1 Model Selection and Implementation
1.3.1.2 Dataset Preprocessing and Feature Engineering
1.3.1.3 Training, Validation, and Testing
1.3.2 AI Models Utilized
1.3.2.1 ML Models
1.3.2.2 DL Models
1.3.2.3 RL Models
1.3.2.4 TL Models
1.3.2.5 Models for ZSL
1.3.3 Datasets
1.3.4 Evaluation Metrics
1.3.5 Cross-Validation and Hyperparameter Tuning
1.3.6 Ethical Considerations
1.4 Results and Comparative Analysis
1.4.1 Performance on Intrusion Detection Tasks
1.4.1.1 NSL-KDD Dataset
1.4.1.2 CICIDS 2017 Dataset
1.4.2 Performance on Malware Detection Tasks
1.4.2.1 EMBER 2018 Dataset
1.4.3 ZSL Results
1.4.4 Summary of Comparative Analysis
1.5 Discussion, Challenges, and Research Gap
1.5.1 Discussion
1.5.2 Challenges and Research Gaps
1.6 Conclusions
References
2. Deep Learning for Cybersecurity
Himani Tyagi, Aditya Dayal Tyagi and Swati Sah
2.1 Introduction
2.1.1 Overview of the Cybersecurity Landscape
2.1.2 Need for Automation and Intelligence in Threat Detection
2.1.3 Why DL for Cybersecurity?
2.2 Fundamentals of DL
2.2.1 Brief Introduction to DL
2.2.2 Common Architectures: CNN, RNN, LSTM, GANs, and Transformers
2.2.3 Convolutional Neural Networks
2.2.4 Recurrent Neural Networks
2.2.5 Long Short-Term Memory Networks
2.2.6 Generative Adversarial Networks
2.2.7 Transformers
2.2.8 Relevance of DL Characteristics to Cybersecurity Tasks
2.2.8.1 Automatic Feature Extraction
2.2.8.2 Scalability to Big Data
2.2.8.3 Adaptability and Generalization
2.2.8.4 Sequential and Temporal Pattern Recognition
2.2.8.5 Robustness to Noisy Data
2.2.8.6 Adversarial Learning and Simulation
2.2.8.7 Real-Time Detection and Decision-Making
2.3 DL Applications in Cybersecurity
2.3.1 Malware Detection
2.3.1.1 Static Analysis versus Dynamic Analysis
2.3.1.2 DL Techniques for Malware Classification
2.3.2 Intrusion Detection Systems
2.3.3 Phishing Detection
2.3.4 Spam and Botnet Detection
2.3.5 CTI Automation
2.3.6 Adversarial Attack Detection
2.3.7 User and Entity Behavior Analytics
2.4 DL Techniques and Architectures in Cybersecurity
2.4.1 CNN for Image-Based Malware
2.4.2 RNN and LSTM for Sequential Logs
2.4.3 Autoencoders for Anomaly Detection
2.4.4 GANs for Attack Simulation and Detection
2.4.5 Transformer Models for Cybersecurity Text Data
2.5 Challenges and Limitations
2.5.1 Adversarial ML
2.5.2 Interpretability and Explainability Issues
2.5.3 Data Scarcity and Data Imbalance
2.5.4 Computational Cost and Model Deployment Hurdles
2.5.5 Privacy Concerns in Cybersecurity Datasets
2.6 Emerging Trends
2.6.1 FL for Distributed Security Systems
2.6.2 SSL and Its Potential
2.6.3 XAI in Cybersecurity
2.6.4 ZTA with AI Assistance
2.6.5 Real-Time Threat Detection Using Edge AI
2.7 Case Studies
2.7.1 Real-World Applications of DL in Cybersecurity
2.7.2 Success Stories and Lessons Learned
2.7.3 Analysis of Some Well-Known Attacks and DL Responses
2.7.3.1 WannaCry Ransomware (2017)
2.7.3.2 SolarWinds Supply Chain Attack (2020)
2.7.3.3 Twitter Bitcoin Scam (2020)
2.7.4 Bridging the Gap between Research and Practice
2.7.5 Conclusion of Case Studies
2.8 Future Directions
2.8.1 Building Resilient AI Models against Adversarial Attacks
2.8.2 Human–AI Collaboration for Cybersecurity
2.8.3 Regulatory Frameworks and Ethical Considerations
2.9 Conclusion
2.9.1 Summary of Key Points
2.9.2 The Evolving Role of DL in Securing Digital Environments
Bibliography
3. Cloud and IoT Security with AI
Amandeep Kaur, Ramandeep Sandhu, Indu Rani, Gaganpreet Kaur and Deepika Ghai
3.1 Introduction
3.2 Integration of AI in People Analytics
3.2.1 Defining People Analytics
3.2.2 AI Technologies in People Analytics
3.2.3 Case Studies
3.2.4 Challenges and Ethical Considerations
3.3 AI-Driven Marketing Strategies
3.3.1 The Evolution of Marketing in the Digital Era
3.3.2 Utilizing Consumer Data with AI
3.3.3 Impact on Sales Performance
3.3.4 Ethical Marketing Practices
3.4 AI in Infrastructure Finance
3.4.1 Overview of Infrastructure Finance
3.4.2 AI-Assisted Risk Evaluation
3.4.3 Data-Driven Decision-Making in Finance
3.4.4 Challenges in Implementation
3.5 Security Challenges in Cloud and IoT Environments
3.5.1 Overview of Security Risks
3.5.2 AI-Driven Security Solutions
3.5.3 Data Intrusion and Authentication Issues
3.5.4 Privacy Concerns and Compliance
3.6 Integrating AI Security Mechanisms
3.6.1 Framework for AI Security in Business Operations
3.6.2 Best Practices for Implementation
3.6.3 Future Trends in AI Security
3.7 Literature Review and Current Trends
3.7.1 Overview of Existing Research
3.7.2 Modern Business Trends
3.7.3 Security Obstacles and Prevention Methods
3.8 Conclusion and Recommendations
3.8.1 Summary of Key Insights
3.8.2 Implications for Businesses and Policymakers
3.8.3 Future Research Directions
References
4. Reinforcement and Deep Learning Approaches to Dynamic
Cache Optimization
Patel Smit Vasant Kumar, Kruti Dataram, Uma Shankar, Sonam Nagpal and Himanshu Amritlal Patel
4.1 Introduction
4.1.1 Traditional Cache Management Techniques and Their Limitations
4.2 Fundamentals of Cache Management
4.2.1 Cache Memory Architecture (L1, L2, L3)
4.2.2 Caching Policies (LRU, LFU, FIFO)
4.2.3 Key Performance Metrics (Hit Ratio, Latency, Throughput)
4.2.4 Challenges in Cache Management in Modern Systems (e.g., Mobile, Cloud, Edge Devices)
4.3 Role of AI in Systems Optimization
4.3.1 AI Techniques Overview
4.4 ML Approaches to Cache Management
4.4.1 Predictive Modeling for Access Patterns
4.4.2 Feature Selection and Importance (e.g., Memory Address Sequences, Access Time)
4.4.3 Evaluation Framework and Datasets (SPEC Benchmarks, Real-World Traces)
4.5 RL for Adaptive Cache Policies
4.5.1 Q-Learning and DQNs for Cache Decisions
4.5.2 Reward Design: Hit/Miss Tradeoffs and Energy Efficiency
4.5.3 Simulation Environment and State Representation
4.5.4 Case Studies and Implementations
4.6 DL in Cache Prefetching and Replacement
4.6.1 Sequence Modeling with Recurrent Neural Networks/Long Short-Term Memorys for Prefetching
4.6.2 Autoencoders for Cache Content Compression
4.6.3 Transformers in Cache Prediction Tasks
4.7 AI in Edge, Cloud, and Multicore Cache Environments
4.7.1 Cache Challenges in Distributed and Heterogeneous Systems
4.7.2 Federated Learning for Cache Management in IoT/Edge
4.7.3 AI in Multicore Cache Coherency Management
References
5. ARKANA: Extracting Knowledge Using Deep Learning for Intrusion Detection From Data Streams
Aravindan V., Rajkanwar Singh, Sanket Mishra and Sandipan Maiti
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.3.1 Data Ingestion and Pipeline
5.3.2 Data Resampling
5.3.3 Feature Selection
5.3.4 Classifiers
5.3.5 Ranking Algorithm
5.3.6 Serving
5.4 Evaluation Metrics
5.5 Results and Discussion
5.5.1 CICEVSE Dataset
5.5.2 ToN_IoT Dataset
5.6 Conclusion
References
6. Blockchain-Enabled Forensic Analysis in Digital Forensics in Cryptography
Preety Shoran, Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Mayur Dattatray Mali and Gauri Mayur Mali
6.1 Introduction to Blockchain-Enabled Forensic Analysis
6.2 Fundamentals of Blockchain Technology in Cryptography
6.3 Challenges in Traditional Digital Forensics in Cryptography
6.4 Integration of Blockchain in Digital Forensics Cryptography
6.5 Blockchain-Based Evidence Management in Cryptography
6.6 Blockchain-Driven Chain-of-Custody Verification
6.7 Enhanced Data Integrity and Authenticity
6.8 Smart Contracts for Automated Forensic Processes
6.9 Decentralized Forensic Investigation Networks
6.10 Case Studies and Practical Implementations
6.11 Challenges and Future Directions
6.12 Conclusion
References
7. Blockchain in Cyber Defense: Strengths, Weaknesses, and Strategic Implications
Pawan Kumar, Sukhvir Singh, Deepika, Ruchi, Jyoti Parashar and Virendra Singh Kushwah
7.1 Introduction
7.1.1 Background
7.1.2 Role of Blockchain in Cybersecurity
7.1.3 Research Objectives
7.1.4 Structure of the Paper
7.2 Fundamentals of Blockchain Technology
7.2.1 Blockchain Architecture
7.2.2 Types of Blockchain
7.2.3 Security Features of Blockchain
7.3 Cybersecurity Risks and Threats
7.3.1 Traditional Cybersecurity Challenges
7.3.2 Blockchain-Specific Security Risks
7.4 Blockchain-Based Cybersecurity Solutions
7.4.1 Enhancing Data Integrity
7.4.2 Identity and Access Management
7.4.3 Preventing Cyberattacks
7.4.4 Smart Contracts for Automated Security
7.5 Comparative Analysis of Blockchain Security Approaches
7.5.1 Overview of Major Blockchain Platforms
7.5.2 Security Strengths and Limitations
7.5.3 Case Studies and Technical Evaluations
7.5.4 Comparative Metrics
7.6 Challenges and Future Research Directions
7.6.1 Regulatory and Compliance Challenges
7.6.2 Interoperability and Integration
7.6.3 Evolving Consensus Mechanisms and Security Frameworks
7.6.4 Future Trends and Research Opportunities
7.7 Conclusion
References
8. Self-Sovereign Identity and Blockchain: Enhancing Privacy and Security in Academic Credential Systems
Omar S. Saleh, Osman Ghazali, Norbik Bashah Idris and Muhammad Ehsan Rana
8.1 Introduction
8.1.1 Background and Context
8.1.2 Motivation for the Chapter
8.1.3 Chapter Objectives
8.2 Theoretical Foundations
8.2.1 Self-Sovereign Identity
8.2.2 Blockchain Technology
8.2.3 The Intersection of SSI and Blockchain
8.3 Current Challenges in Academic Credential Systems
8.3.1 Privacy Concerns
8.3.2 Security Issues
8.3.3 Interoperability and Portability
8.4 SSI and Blockchain in Academic Credential Systems
8.4.1 How SSI and Blockchain Address Current Challenges
8.4.2 Use Cases and Examples
8.4.3 Technical Architecture
8.5 Benefits and Advantages
8.5.1 Enhanced Privacy
8.5.2 Improved Security
8.5.3 Interoperability and Global Recognition
8.5.4 Limitations and Adoption Barriers
8.6 Future Directions
8.6.1 Emerging Trends
8.6.2 Potential Applications beyond Academia
8.6.3 Research Opportunities
8.7 Conclusion
8.7.1 Summary of Key Points
8.7.2 Final Thoughts
Bibliography
9. Blockchain and Federated Learning in IoV: Real-World
Insights into Secure and Decentralized Systems
Ramanjeet Singh, Amandeep Kaur, Divneet Singh Kapoor, Kiran Jot Singh and Khushal Thakur
9.1 Introduction
9.1.1 Introduction of Intelligent Transportation Systems and IoV
9.1.1.1 Intelligent Transportation Systems
9.1.1.2 The IoV
9.1.2 Security, Privacy, Latency, and Data Management Issues in IoV
9.1.2.1 Security Issues in IoV
9.1.2.2 Privacy Concerns
9.1.2.3 Latency Challenges in IoV
9.1.2.4 Data Management in IoV
9.1.3 Blockchain and Federated Learning in IoV
9.1.3.1 The Role of Blockchain in Secure and Decentralized IoV
9.1.3.2 FL for Privacy-Preserving Intelligent IoV Applications
9.2 Fundamentals of Blockchain and FL in IoV
9.2.1 Blockchain in IoV
9.2.1.1 Key Concepts: Decentralization, Immutability, and Smart Contracts
9.2.1.2 Consensus Mechanisms for IoV
9.2.1.3 Security Enhancements Using Blockchain
9.2.2 FL in IoV
9.2.2.1 Concept and Working Mechanism of FL
9.2.2.2 Privacy-Preserving ML Privacy
9.2.2.3 Training Edge or Distributed Model
9.3 Case Studies on Blockchain Applications in IoV
9.3.1 Case Study 1: Secured and Transparent Vehicle Data Sharing
9.3.1.1 Problem Statement: Centralized Data Control and Trust Issues
9.3.1.2 Blockchain Solution: Decentralized Data Exchange and Smart Contracts
9.3.1.3 Implementations and Results
9.3.2 Case Study 2: Fraud Prevention in Vehicular Insurance
9.3.2.1 Problem Statement: Insurance Claim Fraud and Data Manipulation
9.3.2.2 Enabling Transparent Transactions Using Blockchain-Based Smart Contracts
9.3.2.3 Real-World Deployment Scenarios
9.3.3 Case Study 3: Blockchain in Toll Collection and Road Pricing
9.3.3.1 Problems with Traditional Tolling System
9.3.3.2 Automated Toll Collection and Blockchain-Based Micropayments
9.3.3.3 Performance Analysis
9.4 Case Studies on FL in IoV
9.4.1 Case 4: FL for Traffic Prediction and Optimization
9.4.1.1 Necessity of Distributed Learning in Reactive Traffic Control
9.4.1.2 Model Training Using FL across IoV Nodes
9.4.1.3 Comparing the Statistical Performance
9.4.2 Case Study 5: Anomaly Detection in Autonomous Vehicles
9.4.2.1 Fault and Attack Detection in Autonomous Cars with Privacy Preservation
9.4.2.2 FL-Based Cyberattack Detection Model
9.4.2.3 Comparative Analysis of the Anomaly Detection Methods
9.4.3 Case Study 6: Driver Personalized Assistance Systems
9.4.3.1 FL-Based Training of Models While Protecting User Privacy
9.4.3.2 FL-Powered ADAS Performance Evaluation
9.5 Comparison and Analysis of Blockchain with FL in IoV
9.5.1 Strengths and Limitations of Both Approaches
9.5.1.1 Advantages of Blockchain in IoV
9.5.1.2 Drawbacks of Blockchain in IoV
9.5.1.3 Strengths of FL for IoV
9.5.1.4 Limitations of FL in IoV
9.5.2 Possible Synergies between Blockchain and FL
9.5.3 Performance Metrics Comparison
9.6 Challenges and Future Directions
9.6.1 Scalability Challenge of Blockchain and FL in Large-Scale IoV
9.6.2 Security and Privacy Issues
9.6.3 Integrating 5G, Edge Computing, and AI/ML Models
9.6.4 Open Research Challenges
9.7 Concluding Remarks
9.7.1 Summary of Key Insights from Case Studies
9.7.2 Conclusion on Blockchain and FL in IoV
9.7.3 Future Trends and Opportunities
References
10. Securing Decentralized Applications: A Layered Analysis
of Vulnerabilities and Countermeasures in Ethereum dApps
Safia Farooqui, Sonali Gaur, Eshwari Girish Kulkarni, Neha Sharma, Chandan Prasad and Prakash Divakaran
10.1 Introduction
10.1.1 BC-Based Application Layers
10.1.2 Front-End Application Part
10.2 BC Structure Vulnerabilities
10.2.1 BC Forks
10.2.2 Taxonomy of Vulnerabilities in dApps
10.2.2.1 Classification by Source Layer
10.2.2.2 Classification by Impact Severity
10.3 Notorious dApp Exploits and Real-World Case Studies
10.4 Front-End and API Security Risks
10.4.1 Insecure Web3 Provider Injection
10.4.2 Phishing dApps
10.4.3 Insecure HTTP Calls to APIs/Oracles
10.4.4 Session Hijacking and Storage Leaks in MetaMask-Compatible Apps
10.5 Attacks on Oracles and Cross-Chain Bridges
10.5.1 Oracle Price Manipulation
10.5.2 Flash Loan Attacks
10.5.3 Insecure Bridges
10.6 Best Practices for Secure dApp Development
10.6.1 Following OpenZeppelin Standards
10.6.2 Unit Testing with Truffle/Hardhat
10.6.3 User Education and Awareness: The First Line of Defense
10.7 Future Threats and Evolving Attack Vectors
10.7.1 Cryptographic Techniques (e.g., zk-SNARKs, ECDSA, Threshold Signatures) Where Applicable
10.8 Token Standards and Security Comparison
10.9 Future Ethereum Protocol Changes and NFT dApp Security
10.10 Role of End-User Behavior in dApp Vulnerabilities
References
11. Surveying Modern Coverless Steganography: Stable Diffusion and Deep Learning Applications in Cybersecurity
Shaurya Chattopadhyay, Ayan Ghosh, Ayantik Rayand Pabak Indu
11.1 Introduction
11.2 Literature Review
11.3 Conclusion
References
12. Crypto-Genome Deciphering Encryption with Genetic Algorithms in Cyber Defense
G. V. Radhakrishnan, Safia Farooqui, Uma Shankar, Sonam Nagpal, Akhilesh Tiwari and Shitiz Upreti
12.1 Introduction
12.2 Related Works
12.3 Materials and Methods
12.3.1 Problem Statement
12.3.2 Proposed Work
12.3.3 Methodology
12.3.4 Proposed Algorithm
12.3.5 Initialization
12.3.6 Measurement of Physical Fitness
12.4 Experimental Configuration
12.4.1 Parameters Used for Experimentation
12.4.2 Parameters Used for Testing the Experiment
12.5 Empirical Results and Examination
12.6 Conclusion
12.7 Future Work
References
13. Decentralized Finance and Traditional Banking: Transforming Financial Inclusion for Urban Women
Subhagi Saxena and Shivani Chaudhry
13.1 Introduction
13.2 Literature Review
13.2.1 Traditional Banking
13.2.2 AI-Driven DeFi and Blockchain Technology
13.2.3 Blockchain Technology
13.2.4 Financial Inclusion
13.2.5 Women in India
13.3 Methodology
13.3.1 Research Design
13.3.2 Sampling
13.3.3 Variables
13.4 Findings
13.5 Advantages of DeFi with Traditional Banking
13.6 Challenges in the Incorporation of DeFi
13.7 Regulations for DeFi in India
13.8 Policy Implications
13.9 Conclusion
References
14. Overcoming Barriers to AI and Telehealth Adoption: Strategies for Sustainable Integration in Healthcare
Pratibha Garg, Neha Gupta, Nishant Kumar, Shitiz Upreti and Vijayalaxmi Rajendran
14.1 Introduction
14.2 Key Barriers to AI and Telehealth Adoption
14.2.1 Technological Barriers
14.2.1.1 Data Interoperability Challenges
14.2.1.2 System Integration Challenges
14.2.1.3 Scalability of AI and Telehealth Technologies
14.2.1.4 Digital Divide and Unequal Access
14.2.1.5 Security and Privacy Concerns
14.2.2 Human and Cultural Barriers
14.2.3 Ethical and Legal Barriers
14.2.4 Regulatory and Policy Barriers
14.2.5 Economic and Infrastructure Barriers
14.3 Strategies for Overcoming Adoption Barriers
14.3.1 Policy and Regulatory Reforms
14.3.2 Investment in Infrastructure
14.3.3 Workforce Development and Capacity Building
14.3.4 Inclusive and Ethical Design
14.3.5 Building Stronger Governance and Leadership
14.3.6 Public Awareness and Patient Empowerment
14.3.7 Cross-Sector Collaboration
14.4 Case Studies of Successful Implementation
14.4.1 United Kingdom’s National Health Service Digital Transformation
14.4.2 Estonia’s Digital Health System: e-Health and Telemedicine
14.4.3 India’s National Telemedicine Service: eSanjeevani
14.4.4 Sweden’s AI-Driven Healthcare: Region Västra Götaland
14.4.5 Rwanda’s Telehealth Initiative: Rural Care through Digital Health
14.5 Conclusions, Implications, and Future Directions
14.5.1 Conclusions
14.5.2 Implications
14.5.3 Future Directions
References
15. MindLift: A Mental Health Revolution Using AI
Shanky Goyal, Rubal Jeet, Ashima Shahi, Gaurav and Anmol Joshi
15.1 Introduction
15.2 Problem Statement
15.2.1 Current Mental Health Challenges
15.2.1.1 Academic Pressure
15.2.1.2 Social Challenges
15.2.1.3 Emotional Struggles
15.2.1.4 Stigma and Fear
15.2.2 Limitations of Existing Solutions
15.2.2.1 Limited Accessibility
15.2.2.2 Dependence on Self-Reporting
15.2.2.3 Lack of Personalization
15.2.2.4 Limited Emotional Analysis
15.2.2.5 Inconsistent Follow-Up and Support
15.2.2.6 Social Stigma and Hesitation
15.2.2.7 Data Privacy Concerns
15.2.2.8 MindLift’s Approach to Overcoming these Limitations
15.2.3 Why MindLift is Necessary
15.2.3.1 Proactive Identification of Mental Health Issues
15.2.3.2 Personalized Support for Diverse Needs
15.2.3.3 Accessible Anytime, Anywhere
15.2.3.4 Real-Time Emotional Analysis
15.2.3.5 Reducing Dependence on Self-Reporting
15.2.3.6 Anonymity and Safe Space for Expression
15.2.3.7 Seamless Integration with Educational Environments
15.2.3.8 Data Security and Privacy
15.2.3.9 Consistent Monitoring and Follow-Up
15.3 MindLift Features and Functionality
15.3.1 Sentiment Analysis
15.3.1.1 Text Sentiment Analysis
15.3.1.2 Audio Sentiment Analysis
15.3.1.3 Facial Expression Analysis
15.3.1.4 Multimodal Emotion Analysis
15.3.2 Conversational Chatbot
15.3.2.1 Real-Time Emotional Support
15.3.2.2 Coping Strategies
15.3.2.3 Review of Resources
15.3.2.4 Continuous Learning
15.3.2.5 Emergency Assistance
15.3.3 Mood Tracking and Data Insights Tool
15.3.3.1 Daily Mood Logs
15.3.3.2 Behavioral Analysis
15.3.3.3 Personalized Reports
15.3.3.4 Predictive Insights
15.3.4 Privacy and Security
15.3.4.1 End-to-End Encryption
15.3.4.2 Anonymous Mode
15.3.4.3 Regulatory Compliance
15.3.5 Mobile Integration
15.3.5.1 Cross-Platform Access
15.3.5.2 Offline Support
15.3.5.3 Real-Time Notifications
15.3.5.4 Seamless Syncing
15.4 Proposed Solution
15.4.1 Data Processing Layer
15.4.1.1 Data Sources
15.4.1.2 Data Preprocessing
15.4.2 AI Models
15.4.2.1 Natural Language Processing Model
15.4.2.2 Speech Analysis Model
15.4.2.3 Computer Vision Model
15.4.3 Recommendation Engine
15.4.3.1 Coping Strategy Suggestions
15.4.3.2 Personalized Resource Recommendations
15.4.3.3 Continuous Learning
15.4.4 User Interface
15.4.4.1 Conversational Chatbot
15.4.4.2 Mood Tracking Dashboard
15.4.4.3 Data Insights and Reports
15.5 Implementation Strategy
15.5.1 Phase 1: Data Collection
15.5.2 Phase 2: Training and Testing the Models
15.5.3 Phase 3: Mobile App Development
15.5.4 Phase 4: Pilot Testing
15.5.5 Phase 5: Deployment
15.6 Evaluation and Results
15.6.1 Accuracy
15.6.2 User Satisfaction
15.6.3 Engagement
15.6.4 Impact Assessment
15.7 Future Scope
15.7.1 Expanded Emotion Detection
15.7.1.1 Advanced AI Algorithms
15.7.1.2 Multimodal Emotion Recognition
15.7.1.3 Contextual Analysis
15.7.2 Personalized Therapy Suggestions
15.7.2.1 Designing Collaborative Therapy
15.7.2.2 Customized Coping Plans
15.7.2.3 Therapist Connectivity
15.7.2.4 AI-Assisted Therapy Companion
15.7.3 Wearable Integration
15.7.4 Enhanced Data Insights
15.7.5 Community and Peer Support
15.7.6 Global Expansion and Multilingual Support
15.8 Conclusion
References
16. Bibliometric Analysis of Healthcare Metaverse: Trends
and Future Research Directions
Nishant Kumar, Divya Mohan, Neha, Shitiz Upreti and Kamini S. Bhardwaj
16.1 Introduction
16.2 Literature Review
16.3 Methods
16.3.1 Research Design
16.3.2 Data Source: Scopus Database
16.3.3 Search Strategy
16.3.4 Inclusion and Exclusion Criteria
16.3.5 Tools for Bibliometric Analysis
16.3.6 Analytical Framework
16.4 Findings and Discussion
16.4.1 Number of Publications Over Time
16.4.2 Number of Source Citations Over Time
16.4.3 Institutional Contribution Over Time
16.4.4 Countries’ Contribution Over Time
16.4.5 Author’s Contribution in the Field
16.4.6 Highly Cited Publication
16.4.7 Source Document per Year
16.4.8 Most Frequent Keyword
16.4.9 Cocitation Analysis
16.4.10 Coauthorship Analysis
16.4.11 Co-Occurrence Analysis of Keywords
16.4.12 Bibliographic Coupling
16.5 Emerging Themes
16.6 Conclusion
16.6.1 Implications
16.6.2 Limitations and Future Research
References
17. Blockchain in Intelligent Manufacturing Systems
Pallab Banerjee, Ahmad Faraz, Mohit Kumar, Ashwani Kumar and Uma Shankar
17.1 Introduction
17.1.1 IMSs’ History
17.1.2 Blockchain’s Importance in Manufacturing
17.1.3 The Chapter’s Goals
17.2 Literature Review
17.2.1 Supply Chain Management with Blockchain
17.2.2 Blockchain in Industry 4.0 and Smart Factories
17.2.3 Using Blockchain for Predictive Maintenance and Quality Assurance
17.2.4 Difficulties and Restrictions
17.2.5 The Literature’s Gaps
17.3 Blockchain’s Technical Underpinnings in Manufacturing
17.3.1 Fundamental Blockchain Technology Elements
17.3.2 Manufacturing Blockchain Architectures
17.3.3 Industry 4.0 Technology Integration
17.3.4 Manufacturing Smart Contracts
17.3.5 Data Integrity and Security
17.3.6 Performance and Scalability Considerations
17.4 Blockchain Applications for Intelligent Manufacturing
17.4.1 Traceability of the Supply Chain
17.4.2 Assurance and Quality Control
17.4.3 Maintenance Prediction
17.4.4 Teamwork in Manufacturing
17.4.5 Protection of Intellectual Property
17.4.6 New Uses
17.5 Case Studies
17.5.1 Case Study 1: Blockchain in Ford and IBM’s Automotive Supply Chain
17.5.2 Case Study 2: Merck Pharmaceutical Manufacturing Quality Control
17.5.3 Case Study 3: Boeing’s Aerospace Predictive Maintenance
17.5.4 Case Study 4: ManuChain’s Collaborative Manufacturing in SMEs
17.5.5 Knowledge Acquired
17.6 Challenges and Limitations
17.6.1 Problems with Scalability
17.6.2 Compatibility with Current Systems
17.6.3 Energy Consumption
17.6.4 Adoption Expenses and Difficulties
17.6.5 Legal and Regulatory Obstacles
17.6.6 Collaboration among Stakeholders and Trust
17.6.7 Techniques for Overcoming Obstacles
17.7 Future Directions and Recommendations
17.7.1 Using Emerging Technologies in Integration
17.7.2 Interoperability and Standardization
17.7.3 Energy Efficiency and Scalability
17.7.4 Frameworks for Policies and Regulations
17.7.5 Adoption and Workforce Development
17.7.6 Prospects for Research
17.7.7 Implementation Suggestions
17.8 Conclusion
References
Index
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