Browse Subject Areas

For Authors

Cryptographic Vulnerabilities and Blockchain Forensics in Cybersecurity

Edited by Chandra Singh, K.V.S.S.S.S Sairam, Shwetha N., Roopesh Ramesh, Arun Upadhyaya, and Tanya Mendez
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394490318 | Hardcover |
524 pages
Price: $225 USD

One Line Description
This essential guide equips cybersecurity professionals, investigators, and blockchain architects with the theoretical insights and practical, battle-tested tools needed to expose cryptographic flaws, investigate decentralized cybercrimes, and stay ahead of the next generation of blockchain threats.

Description
As blockchain adoption accelerates across the financial, governmental, and industrial sectors, new avenues for cybercrime have emerged, ranging from smart contract exploits and token theft to the obfuscation of illicit transactions through decentralized platforms. This book explores the intersection of cryptographic security flaws and the growing complexities of blockchain technology within the context of modern cyber threats. It critically examines how cryptographic protocols, while foundational to digital trust, can also harbor subtle implementation weaknesses that cybercriminals exploit to compromise systems or evade detection. Adopting an interdisciplinary approach, the book blends principles of cryptography, blockchain architecture, digital forensics, and cybersecurity law. It provides both theoretical frameworks and practical tools for investigating blockchain-based cyber incidents, such as tracing cryptocurrency transactions on public ledgers, identifying forensic markers in smart contract breaches, and analyzing side-channel attacks on cryptographic algorithms. The book also addresses the real-world implications of these vulnerabilities, including regulatory gaps, privacy concerns, and the ethical use of forensic techniques in decentralized ecosystems, through case studies drawn from actual investigations that help bridge theory and practice. By highlighting the dynamic interplay between innovation and risk in the blockchain domain, this book equips readers with the skills and mindset necessary to confront the next generation of cryptographic and blockchain-based cyber threats.

Back to Top

Author / Editor Details
Chandra Singh is an Assistant Professor in the Department of Electronics and Communication at the Nitte Mahalinga Adyantaya Memorial Institute of Technology, Nitte, India ,with more than six years of experience. He has published nine books, 15 book chapters, more than 20 research articles in reputed peer-reviewed national and international journals, five international patents, and six national patents. His areas of interest include optical networking and communication, wireless communication, intelligent sytems, IoT, and robotics.

K.V.S.S.S.S Sairam, PhD is a Professor and Head of the Department of Electronics and Communication Engineering at the Nitte Mahalinga Adyantaya Memorial Institute of Technology, Nitte, India. He has published more than 60 papers in international journals and conferences of repute and five books, and has been granted five patents and two grants. His research areas are optical communications, optical networks, and wireless communication.

Shwetha N., PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the Dr. Ambedkar Institute of Technology, Bengaluru. With more than a decade of experience in academics and research, she has published more than 34 research articles in reputed peer-reviewed national and international journals, presented ten papers at national and international conferences, and holds two national patents. Her research interests span nature-inspired algorithms, image and video processing, and audio and speech signal processing.

Roopesh Ramesh, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the Dr. Ambedkar Institute of Technology, Bengaluru, with more than six years of experience in academics and research. He has published three journal articles, presented more than ten papers in national and international conferences, and attended more than five conferences, seminars, and workshops. His research interests include cooperative communications, 5G, 6G and beyond 6G communications, wireless communications, hybrid communications, and power line communications.

Arun Upadhyaya is an Associate Professor and Head of the Department in the Department of Electronics and Communication at the Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi. He has published five articles in international journals and conferences of repute. His research interests include cryptography, network security, image processing, and communication engineering.

Tanya Mendez, PhD is an Assistant Professor in the Department of Robotics and Artificial Intelligence at the Nitte Mahalinga Adyantaya Memorial Institute of Technology, Nitte, with more than eight years of experience. She has published more than 15 papers in international journals and conferences. Her research interests include digital system design, low-power VLSI design, electronic design automation, and VLSI architecture design.

Table of Contents
Preface
Part 1: Cryptography and Security Foundations
1. Crypto Wallet Security and Token Theft Detection Using Machine Learning
Dankan Gowda V., Rakesh V. S., Alpha Vijayan, K. Sri Yogi and Negalurmath Vinayakaswami
1.1 Introduction
1.2 Background and Literature Survey
1.3 Machine Learning in Security
1.4 Methodology
1.5 Results
1.6 Case Studies
1.7 Discussion
1.8 Conclusion
References
2. Symmetric and Asymmetric Cryptography for Blockchain
Security in IoT Systems
Dankan Gowda V., M. Pravin, Madan Mohanrao Jagtap, Sulaiman R. and K.D.V. Prasad
2.1 Introduction
2.2 Literature Survey
2.3 Cryptographic Algorithms for Blockchain Security in IoT
2.4 Results and Discussion
2.5 Future Directions
2.6 Conclusion
References
3. Advanced Cryptographic Techniques in Hardware Security:
Enhancing Data Protection
Mandeep Kaur
3.1 Introduction
3.2 Background on Cryptography
3.3 Types of Advanced Cryptographic Techniques
3.4 Cryptographic Algorithm Simulations in MATLAB
3.5 Security Challenges and Countermeasures
3.6 Results
3.7 Conclusion
References
4. Machine Learning Techniques in Blockchain Forensics for Crypto Crime Detection
Dankan Gowda V., Sajja Suneel, Madan Mohanrao Jagtap, K.D.V. Prasad and P. Mohana
Introduction
Literature Survey
Methodology
Data Collection
Feature Engineering
Model Selection
Evaluation Metrics
Results
Model Performance
Case Study Outcomes
Challenges Encountered
Discussion
Interpretation of Results
Implications for Forensic Practices
Limitations
Future Directions
Conclusion
References
5. Symmetric and Asymmetric Algorithms: Exploits and Limits
Arun Upadhyaya, Chandra Singh, Ganesh Shetty, Ranjan Kumar and Nagaraja Rao
5.1 Introduction
5.1.1 Definition and Importance of Cryptographic Algorithms in Modern Security
5.1.2 Overview of Symmetric and Asymmetric Algorithms
5.2 Symmetric Algorithms
5.2.1 Mechanisms and Algorithms
5.2.2 Common Symmetric Algorithms
5.2.3 Benefits and Uses
5.2.4 Limitations and Vulnerabilities
5.3 Asymmetric Encryption
5.3.1 Public-Key Cryptography and Its Operation
5.3.2 Mathematical Model (RSA Example)
5.3.3 Advantages
5.3.4 Drawbacks and Challenges
5.4 Exploits of Algorithms
5.4.1 Ordinary Exploits and Attacks on Symmetric and Asymmetric Algorithms
5.4.2 Case Studies for Notable Attacks or Vulnerabilities
5.4.3 Risk Mitigation Strategies (e.g., Algorithm Combination)
5.5 Comparative Analysis
5.6 Future Directions and Innovations
5.6.1 Emerging Trends in Cryptographic Solutions
5.6.2 Technological Advancement and Research Direction
5.7 Conclusion
Bibliography
6. Emerging Trends in Post-Quantum Cryptography and Blockchain Resilience
Roopesh Ramesh, Priyanka S. and Kalyan N.
6.1 Introduction
6.1.1 Importance of Cryptography in Securing Communication, Authentication, and Blockchains
6.1.2 The Quantum Threat: Shor’s and Grover’s Algorithms
6.1.3 Motivation for Post-Quantum Cryptography (PQC) and Blockchain Resilience
6.1.4 The Broader Perspective: Beyond Cryptography Alone
6.1.4.1 Hybrid Models: Integrating Classical and Post-Quantum Cryptography
6.1.4.2 Quantum-Resistant Consensus Protocols
6.1.4.3 Scalability Considerations: Balancing Security and Efficiency
6.1.4.4 Governance and Interoperability
6.1.4.5 Quantum Resilience as a Technical and Social Challenge
6.1.5 Structure of the Chapter
6.2 The Quantum Threat Landscape
6.2.1 Overview of Quantum Computing Progress (Global Research and Prototypes)
6.2.2 Cryptographic Vulnerabilities Under Quantum Attacks
6.2.2.1 RSA, ECC, and Diffie–Hellman
6.2.2.2 Digital Signatures and Authentication Risks
6.2.2.3 Transaction Integrity in Blockchain Networks
6.2.2.4 The “Harvest Now, Decrypt Later” Problem
6.2.3 Implications for Cryptocurrencies, Smart Contracts, and Decentralized Applications
6.2.4 The Role of Standardization: NIST PQC Timeline
6.3 Post-Quantum Cryptography (PQC): Foundations and Families
6.3.1 Introduction to PQC and its Design Goals
6.3.2 Algorithmic Families of PQC
6.3.2.1 Lattice-Based Cryptography
6.3.2.2 Code-Based Cryptography
6.3.2.3 Hash-Based Cryptography
6.3.2.4 Multivariate Polynomial Cryptography
6.3.2.5 Isogeny-Based Cryptography
6.3.3 Comparative Analysis: Security, Efficiency, and Scalability
6.3.4 Current NIST Standardization Efforts and Global Initiatives
6.4 Integrating PQC into Blockchain Systems
6.4.1 Role of Cryptography in Blockchain
6.4.2 Potential Applications of PQC in Blockchain
6.4.2.1 Secure Transaction Authentication
6.4.2.2 Post-Quantum Consensus Protocols
6.4.2.3 Resilient Smart Contracts
6.4.3 Performance Considerations
6.4.4 Balancing Scalability and Resilience in Decentralized
Networks
6.5 Beyond Algorithms: Blockchain Resilience Strategies
6.5.1 Hybrid Security Models
6.5.2 Quantum Key Distribution (QKD): Opportunities and Challenges
6.5.3 Consensus Innovations: Quantum-Resistant Mechanisms
6.5.4 Architectural Adaptations
6.5.5 Zero-Knowledge Proofs in a Post-Quantum World
6.6 Real-World Initiatives and Pilot Projects
6.6.1 Case Studies on PQC-Enabled Blockchains
6.6.2 Prototypes of Quantum-Resistant Wallets and Signature Schemes
6.6.3 Test Networks Experimenting with Hybrid Cryptography
6.6.4 Cross-Disciplinary Collaborations
6.6.5 Lessons Learned in Early Adoption
6.7 Challenges and Open Issues
6.7.1 Computational and Storage Overhead of PQC Algorithms
6.7.2 Interoperability and Standardization across Blockchains
6.7.3 Governance and Migration Difficulties in Decentralized Ecosystems
6.7.4 Balancing Efficiency, Security, and Usability
6.7.5 Uncertainty of Quantum Timelines and Risk of Premature Adoption
6.8 Future Directions
6.8.1 Efficient PQC Algorithms for High-Throughput Systems
6.8.2 Layered Security and Integrated Resilience Architectures
6.8.3 Long-Term Integration of PQC with Blockchain Innovations
6.8.4 Policy and Regulatory Frameworks for Quantum-Secure Systems
6.8.5 Developer, Enterprise, and Government Education and Awareness Programs
6.9 Conclusion
6.9.1 Restatement of the Need to Prepare for Quantum Threats
6.9.2 Overview of PQC’s Function in Protecting Blockchain
Resilience
6.9.3 Broader Ecosystem View: Cryptography, Architecture, and Governance
6.9.4 Call for Cooperative and Initiative Action across Disciplines
6.9.5 Vision of a Secure, Quantum-Resilient Blockchain Future
References
7. Advances in Post-Quantum Cryptography and Blockchain
Resilience
Sandeep Kumar Hegde, Rajalaxmi Hegde and Thangavel Murugan
7.1 Introduction
7.2 Literature Review
7.3 Methodology
7.4 Experimental Results
7.5 Conclusion
References
Part 2: Blockchain Forensics and Cybercrime Investigation
8. Machine Learning for Cryptanalysis and AI-Driven Security
Solutions
Neha Patwari, Archita Agar, Nidhi Bhavsar, Kriti Das and Apeksha Waghmare
8.1 Introduction
8.2 Literature Survey
8.3 Methodology
8.4 Results
8.5 Discussion
8.6 Conclusion
References
9. Investigating Cryptographic Vulnerabilities and Blockchain Evidence in Cybersecurity
Prajna U.R., Brinda R. Shetty, Prathik Poojary, Shivani K. and Vaishnav L.
9.1 Introduction
9.1.1 Overview
9.1.2 Problem Statement
9.1.3 Objectives
9.2 Literature Survey
9.3 Methodology
9.3.1 Workflow Process
9.4 Design Flow and Model Implementation
9.4.1 Model Implementation
9.5 Results and Discussion
9.5.1 Strengths of the Proposed Framework
9.5.2 Limitations and Challenges
9.5.3 Future Improvements
9.6 Conclusion
References
10. Blockchain Forensics in Cybersecurity: Challenges, Tools, Techniques, and Applications
Srinivas Mishra and Monisha Gupta
10.1 Introduction
10.1.1 An Overview of Blockchain Forensics
10.1.2 Role in Law Enforcement
10.1.3 Differences between Digital Forensics and Blockchain Forensics
10.2 Blockchain Forensic Techniques
10.2.1 Challenges in Blockchain Forensics
10.3 Blockchain Forensic Tools
10.3.1 Bitquery – Coinpath API
10.3.2 Chainalysis
10.3.3 Elliptic
10.3.4 CipherTrace
10.3.5 Crystal Blockchain
10.3.6 TRM Labs
10.3.7 BlockSci
10.3.8 Scorechain
10.3.9 Elementus
10.3.10 Coinfirm
10.3.11 How to Choose the Best Forensics Tool?
References
11. Deep Learning and Graph Analytics in Crypto Wallet Clustering and Attribution Techniques for Blockchain Forensics
Rajalaxmi Hegde, Sandeep Kumar Hegde and Thangavel Murugan
11.1 Introduction
11.2 Basics of Blockchain Transactions Graph
11.2.1 UTXO and Account-Based Models
11.2.2 Graph Construction and Preprocessing
11.3 Classical Clustering Heuristics
11.3.1 Common-Input-Ownership (CIO) Heuristic
11.3.2 Change-Address Heuristics
11.3.3 CoinJoin Detection and De-Mixing
11.4 Machine Learning and Graph Neural Network Approaches
11.4.1 Conventional Machine Learning Classifiers
11.4.2 Graph Convolutional Networks (GCN)
11.4.3 Graph Attention Networks (GAT)
11.4.4 Temporal Graph Networks and Dynamic Clustering
11.5 Attribution Pipeline: From Clusters to Entities
11.5.1 On-Chain Intelligence Gathering
11.5.2 Off-Chain Data Integration
11.5.3 Attribution Confidence Scoring
11.6 Privacy-Enhancing Technologies and Countermeasures
11.6.1 Mixing Services and Tumblers
11.6.2 Privacy Coins Based on a Zero-Knowledge Proof
11.6.3 Cross-Chain Bridges and Layer-2 Protocols
11.7 Experimental Results and Analysis
11.7.1 Dataset Description
11.7.2 Experimental Setup
11.7.3 Results and Discussion
11.8 Legal, Ethical, and Regulatory Considerations
11.8.1 Law Enforcement Applications and Case Studies
11.8.2 AML Compliance and Travel Rule
11.8.3 Ethical Constraints and Privacy Rights
11.9 Future Directions and Open Challenges
11.10 Conclusion
References
12. Blockchain Forensics: Tools, Techniques, AI Architectures, and Cross-Chain Intelligence for Modern Crypto Crime Investigation
Kashyap C. Patel, Sachin A. Goswami and Saurabh A. Dave
12.1 Introduction
12.1.1 Technical Foundation
12.1.2 The Development of Forensic Capabilities: From Institutional Intelligence to Manual Tracing
12.1.3 DeFi Expansion and Cross-Chain Investigative Complexity
12.1.4 Bitcoin’s Forensic Paradox: Dominance, Liquidity, and Traceability
12.1.5 Regulatory Convergence and AML/CFT Integration
12.2 Evolution of Blockchain Forensics: A Decade of Transformation
12.3 Core Methodologies and Technical Pillars of Blockchain Forensics
12.3.1 Transaction Graph Modeling and Network Analytics
12.3.2 Address Clustering and Attribution Heuristics
12.3.3 Taint Propagation and Exposure Analysis
12.3.4 Composite Risk Scoring Frameworks
12.3.5 Cross-Chain Correlation and Bridge Analysis
12.3.6 AI-Driven Anomaly Detection and Predictive Analytics
12.3.7 Integration with Regulatory and Recovery Workflows
12.4 AI-Driven Architectures and Operational Intelligence in
Modern Blockchain Forensics
12.4.1 System Architecture: Layered Intelligence Frameworks
12.4.2 Machine Learning Models in Forensic Intelligence
12.4.3 Cross-Chain Analytics and Interoperability Monitoring
12.4.4 Risk Scoring Engines and Behavioral Intelligence
12.4.5 Institutionalization and Strategic Investment in Forensic Infrastructure
12.4.6 Transition from Reactive Investigation to Predictive
Enforcement
12.5 Integration of Regulations, Compliance Structures, and
Convergence of Global Governance
12.5.1 Alignment with AML/CTF of Terrorism Frameworks
12.5.2 Sanctions Enforcement and Network-Based Exposure Monitoring
12.5.3 SAR and Investigative Documentation
12.5.4 Asset Recovery and Enforcement Coordination
12.5.5 Institutional Investment and Governance Standardization
12.5.6 Global Regulatory Harmonization and Future Directions
12.6 Emerging Challenges, Limitations, and Future Directions
in Blockchain Forensics
12.6.1 Privacy-Enhancing Technologies and Obfuscation Protocols
12.6.2 Cross-Chain Fragmentation and Bridge Exploitation
12.6.3 Scalability and Data Volume Constraints
12.6.4 Attribution Uncertainty and False Positives
12.6.5 Regulatory Divergence and Jurisdictional Fragmentation
12.6.6 Adversarial Adaptation and AI Evasion
12.6.7 The Future of Predictive Blockchain Intelligence
12.7 Case Study: Squid Game Token Rug Pull – Graph-Based Forensic Reconstruction and Liquidity Drain Detection
12.7.1 Introduction to the Case
12.7.2 Phase I: Malicious Smart Contract Deployment
12.7.3 Phase II: Liquidity Drain Detection (Mathematical Trigger)
12.7.4 Phase III: Fund Aggregation and Graph Expansion
12.7.5 Phase IV: Mixer Interaction and Taint Propagation
12.7.6 Phase V: Exit Strategies – DEX and CEX Monitoring
12.7.7 Risk Scoring and AI-Based Detection
12.7.8 Forensic Significance and Lessons Learned
12.8 Computational Details of Blockchain Forensic Intelligence
12.8.1 Centrality-Based Suspicion Modeling
12.8.2 Taint Propagation Model
12.8.3 Composite Risk Scoring Function
12.8.4 Liquidity Exploit Detection Model
12.8.5 Cross-Chain Correlation Function
12.8.6 Anomaly Detection Model
12.8.7 Computational Complexity Considerations
12.9 Prospects for Future Research and a Strategic Plan for
Blockchain Forensic Intelligence
12.9.1 Explainable AI and Evidentiary Robustness
12.9.2 Advanced Cross-Chain Intelligence and Interoperability Monitoring
12.9.3 Predictive Fraud Detection and Behavioral Forecasting
12.9.4 Privacy-Preserving Forensics and Ethical Boundaries
12.9.5 Scalability, Automation, and Real-Time Enforcement
12.9.6 Global Regulatory Harmonization and Institutional Collaboration
12.9.7 Quantum Risk and Cryptographic Evolution
12.10 Integration with AML and the Wolfsberg Framework
12.11 Investigative Workflow Model in Blockchain Forensics
12.12 Challenges and Limitations in Blockchain Forensic Intelligence
12.12.1 Privacy Coins
12.12.2 Mixers
12.12.3 Cross-Jurisdiction Barriers
12.12.4 Data Volume and Scalability
12.12.5 Explainability and Legal Defensibility
12.13 Future Directions, Strategic Investment, and the Evolution of Blockchain Forensic Intelligence
12.13.1 Future Directions in Blockchain Forensics
12.13.2 Strategic Investments and Industry Collaboration
12.13.3 From Transparency to Intelligence
12.14 Conclusion
References
Part 3: IoT, Systems Security, and Deployment Strategies
13. Signal Processing and Cryptography in the Age of IoT to Enhance Device Security and Privacy
Devendra Joshi, Annepu Arudra, Galiveeti Poornima, K.D.V. Prasad and Dankan Gowda V.
13.1 Introduction
13.2 Signal Processing Techniques for IoT Security
13.3 Cryptography in IoT Security
13.4 Signal Processing and Cryptography for Enhanced IoT Security
13.5 Challenges and Limitations
13.6 Future Trends and Innovations
13.7 Conclusion
References
14. Defensive Strategies and Best Practices in Secure Blockchain Deployment
Smitha Gayathri D., Kumar P., Divya D., Soumya Prasad and Santhosh Kumar R.
14.1 Introduction
14.1.1 Consensus Algorithm
14.1.2 Smart Contract
14.1.3 Cryptography for Blockchain
14.2 Blockchain Challenges
14.2.1 Lack of Adoption
14.2.2 Skills Gap
14.2.3 Trust Among Users
14.2.4 Financial Resources
14.2.5 Blockchain Interoperability
14.2.6 Slow Development Pace
14.2.7 Lack of Regulation
14.3 Blockchain Applications
14.3.1 Cryptocurrencies
14.3.2 Supply Chains
14.3.3 Smart Dubai Office
14.4 Security Risks and Attacks with Blockchain
14.4.1 Security Risks Associated with Blockchain
14.4.2 Real Attacks and Bugs on Blockchain Systems
14.4.2.1 Core Software Bug
14.4.2.2 Attacks on the Cryptocurrency Exchange Platforms
14.4.2.3 Attacks with Wallets
14.4.2.4 Network Attacks
14.4.2.5 Endpoint Attacks
14.5 Security Measures in Blockchain
14.5.1 Security Analysis
14.5.2 Detecting Malicious Codes and Bugs
14.5.3 Core Software Code Security
14.5.4 Secure Smart Contract
14.5.5 Smart Contract Verification
14.5.6 Privacy Preserving
14.5.7 Monitoring and Regulations against Hackers’ Wallets
14.5.8 Hard Fork
14.6 Conclusion
References
15. Signal Processing and Machine Learning for Blockchain
Security in IoT Networks
Dankan Gowda V., C.R.Vijay, Sadashiva V. Chakrasali, K.D.V. Prasad and M.S. Parkavi
15.1 Introduction
15.2 Background and Fundamentals
Signal Processing in IoT
Machine Learning Applications
Blockchain Technology
15.3 Literature Survey
IoT Security Challenges
Signal Processing Techniques
Machine Learning Approaches
Blockchain Implementations
Integration Models
Emerging Trends
15.4 Methodology
System Architecture
Data Flow and Processing
Security Protocols
15.5 Results and Discussion
Performance Metrics
Case Study Analysis
Comparative Analysis
Challenges and Limitations
15.6 Future Directions
Advancements in ML Algorithms
Blockchain Innovations
Integration with Edge and Fog Computing
15.7 Conclusion
References
Part 4: Applied Security Systems and Case Studies
16. An Explainable and Resource-Efficient AI Framework for
Real-Time Cyber-Threat Detection in Edge Networks
Jabarullah, Sonya, Mohamed Arshad and Sachithanantham
16.1 Introduction
16.2 Objectives
16.3 Literature Review
16.3.1 International Research Landscape
16.3.2 National Research Trends (India)
16.4 Problem Formulation
16.4.1 System Architecture
16.4.2 Model Architecture
16.4.3 Explainability Module
16.4.3.1 SHAP-Based Feature Attribution
16.5 Simulation Setup
16.5.1 Datasets
16.5.2 Experimental Environment
16.5.3 Evaluation Metrics
16.6 Results and Analysis
16.6.1 Classification Performance
16.6.2 Confusion Matrix Analysis
16.6.3 ROC Curve Analysis
16.6.4 Detection Accuracy Comparison
16.6.5 Inference Time Analysis
16.7 Explainability Analysis
16.7.1 SHAP Feature Importance
16.7.2 Attention Heatmap Interpretation
16.8 Additional Comparative Analysis
16.8.1 False-Positive Rate
16.8.2 Multi-Dimensional Radar Analysis
16.8.3 Memory Footprint
16.9 Federated Learning Analysis
16.9.1 Training Convergence
16.9.2 Federated Round Analysis
16.10 Implication and Significance
16.11 Applications
16.12 Challenges
16.13 Future Study
16.14 Conclusion
Bibliography
17. MediSentry: An AI-Powered Clinical Decision Support System for Medication Safety and Risk Prediction
Lakkshanya Suresh, Harshini Prasanna K., Pranav Rajesh and Jayalakshmi P.
17.1 Introduction
17.2 Literature Survey
17.3 Problem Statement
17.4 Proposed Work
17.5 Methodology
17.5.1 Requirement Analysis
17.5.2 System Approach
17.5.3 System Architecture
17.5.4 Model Architecture
17.5.4.1 DDI Deep Learning Model
17.5.4.2 Gradient Boosting Risk Prediction Model
17.5.4.3 Expert Rule-Based Clinical Engine
17.5.4.4 RAG Clinical Explainer
17.5.4.5 LSTM-Based AMR Forecasting
17.5.5 System Workflow
17.6 Implementation
17.6.1 System Overview
17.6.2 Front-End Development
17.6.3 Backend API and Orchestration
17.6.4 Data Management and Preprocessing
17.6.5 AI Model Integration
17.7 Results and Discussion
17.8 Advantages
17.9 Future Enhancement
17.10 Conclusion
Bibliography
18. Multi-Platform Social Media Engagement Analysis: An
Empirical Study of User Interaction Patterns Using Social
Network Data Analytics
Prachi Patil and Shubhangi Kamble
18.1 Introduction
18.2 Literature Review
18.2.1 Introduction
18.2.2 Structural Properties of Social Networks
18.2.3 Community Detection in SNDA
18.2.4 Analyzing Social Influence and Information Diffusion
18.2.5 Big Data Analytics and Social Media
18.2.6 Application of Social Network Data Analytics
18.2.7 Method of Analysis: Social Network Analysis
18.2.8 Issues with Analyzing Social Networks Data
18.3 Research Gap and Objectives
18.4 Methodology
18.4.1 Data source
18.4.2 Data Preprocessing
18.4.3 Engagement Metric Construction
18.4.4 Data Analysis Techniques
18.5 Data Analysis/Result
18.5.1 Platform-Wise Average Engagement
18.5.2 Topic-Wise Engagement Analysis
18.5.3 Engagement Distribution across Platforms
18.5.4 Component-Wise Engagement Analysis
18.5.5 Correlation between Engagement Metrics
18.5.6 Interpretation in the Context of Research Objectives
18.6 Discussion
18.6.1 Interpretation of Result
18.6.2 Comparison with Existing Literature
18.6.3 Practical Implications
18.7 Conclusion
References
19. Government Fund Allocation and Tracking Using Blockchain
Madhu B. and Kiran M.
19.1 Introduction
19.2 Literature Survey
19.3 Existing System
19.4 Proposed System
19.5 Results
19.6 Conclusion
References
Index

Back to Top

Description
Author/Editor Details
Table of Contents
Bookmark this page

Search

Browse Book Series

Browse Subject Areas

For Authors

Cryptographic Vulnerabilities and Blockchain Forensics in Cybersecurity