Innovative Machine Learning Applications for Cryptography 9798369316429, 9798369316436

Table of contents :
Cover
Title Page
Copyright Page
Book Series
Mission
Coverage
Preface
ORGANIZATION OF THE BOOK
IN CONCLUSION
Chapter 1: Introduction to Modern Cryptography and Machine Learning
ABSTRACT
1. INTRODUCTION
2. THE NEED FOR MODERN CRYPTOGRAPHY
3. COURSES IN MACHINE LEARNING
4. CONVERGENCE OF CRYPTOGRAPHY AND MACHINE LEARNING
5. MACHINE LEARNING-BASED CRYPTANALYSIS
6. MACHINE LEARNING FOR CRYPTOGRAPHIC SECURITY
7. ETHICAL AND PRIVACY ISSUES
8. FUTURE DIRECTIONS AND CHALLENGES
9. CONCLUSION
REFERENCES
Chapter 2: Future Outlook
ABSTRACT
1. INTRODUCTION
2. THE EVOLVING ROLE OF AI IN CRYPTOGRAPHY
3. STRENGTHENING AI AGAINST EMERGING THREATS
4. COLLABORATIVE INNOVATIONS IN CRYPTOGRAPHIC PROTOCOLS
5. PRIVACY-PRESERVING AI
AUTONOMOUS CRYPTOGRAPHIC SYSTEMS
7. POST-QUANTUM SECURITY
8. FUTURE HORIZONS
9. CONCLUSION
REFERENCES
Chapter 3: Artificial Intelligence-Supported Bio-Cryptography Protection
ABSTRACT
1. INTRODUCTION
2. LITERATURE REVIEW
3. SECURITY MODELS IN PRACTICES
4. MACHINE LEARNING AND CRYPTOGRAPHY
5. BIOMETRICS AUTHENTICATION AND MACHINE LEARNING
6. CONCLUSION AND FUTURE STUDY
REFERENCES
ADDITIONAL READING
Chapter 4: An Adaptive Cryptography Using OpenAI API
ABSTRACT
1. INTRODUCTION
2. BACKGROUND
3. PROCESSING WAYS
4. SYSTEM ARCHITECTURE
5. PARAMETERS INFLUENCING ACCURACY
6. FUTURE RESEARCH DIRECTIONS
7. CONCLUSION
REFERENCES
Chapter 5: Optimized Deep Learning-Based Intrusion Detection Using WOA With LightGBM
ABSTRACT
1 INTRODUCTION
2 LITERATURE REVIEW
3 MATERIALS AND METHODS
4. DATASET AND METHODOLOGY
5. EXPERIMENTS AND RESULTS
6. CONCLUSION
ABBREVIATIONS
REFERENCES
Chapter 6: A Survey of Machine Learning and Cryptography Algorithms
ABSTRACT
INTRODUCTION
LITERATURE SURVEY
RESEARCH GAP
PROPOSED METHOD
CONTRIBUTION TO THE RESEARCH
MACHINE LEARNING AND ITS APPLICATIONS
APPLICATIONS IN CRYPTOGRAPHY
ATTACKS ON MACHINE LEARNING IN CRYPTOGRAPHY
Results
CONCLUSION AND FUTURE WORKS
FUTURE WORKS
REFERENCES
Chapter 7: Quantum Cryptography
ABSTRACT
INTRODUCTION
BACKGROUND
RELATED WORKS
METHODOLOGY
QUANTUM KEY DISTRIBUTION (QKD) ALGORITHMS
QDS (QUANTUM DIGITAL SIGNATURES)
RESULTS AND DISCUSSION
CONCLUSION
REFERENCES
Chapter 8: Minimizing Data Loss by Encrypting Brake-Light Images and Avoiding Rear-End Collisions Using Artificial Neural Network
ABSTRACT
1. INTRODUCTION
2. RELATED WORK
3. METHODOLOGY
4. RESULTS AND DISCUSSION
5. CONCLUSION
REFERENCES
Chapter 9: Machine Learning Techniques to Predict the Inputs in Symmetric Encryption Algorithm
ABSTRACT
INTRODUCTION
BACKGROUND
METHODOLOGY
RESULT AND DISCUSSION
CONCLUSION
REFERENCES
Chapter 10: Homomorphic Encryption and Machine Learning in the Encrypted Domain
ABSTRACT
1. INTRODUCTION TO HOMOMORPHIC ENCRYPTION
2. HOMOMORPHIC ENCRYPTION TECHNIQUES
3. MACHINE LEARNING IN THE ENCRYPTED DOMAIN
4. INTEGRATION OF HOMOMORPHIC ENCRYPTION WITH MACHINE LEARNING
5. PRACTICAL APPLICATIONS AND CASE STUDIES
6. CHALLENGES AND LIMITATIONS
7. FUTURE PROSPECTS
8. CONCLUSION
REFERENCES
Chapter 11: An Effective Combination of Pattern Recognition and Encryption Scheme for Biometric Authentication Systems
ABSTRACT
1. INTRODUCTION
2. RELATED WORK
3. METHODOLOGY
4. RESULTS AND DISCUSSION
5. CONCLUSION
REFERENCES
Chapter 12: Enhancing Crypto Ransomware Detection Through Network Analysis and Machine Learning
ABSTRACT
1. INTRODUCTION
2. RELATED WORK
3. METHODOLOGY
4. RESULTS AND DISCUSSION
5. CONCLUSION
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 13: A Survey of Innovative Machine Learning Approaches in Smart City Applications
ABSTRACT
1. INTRODUCTION
2. BACKGROUND AND MOTIVATION
3. PROPOSED METHODOLOGY
4. FUTURE SCOPE
5. CONCLUSION
REFERENCES
Chapter 14: Securing the IoT System of Smart Cities by Interactive Layered Neuro-Fuzzy Inference Network Classifier With Asymmetric Cryptography
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. RESEARCH GAP
4. PROPOSED SYSTEM
5. RESULTS AND DISCUSSIONS
6. CONCLUSION
REFERENCES
Compilation of References
About the Contributors