Innovative Machine Learning Applications for Cryptography

Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, machine learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches. 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