Machine Learning and Cryptographic Solutions for Data Protection and Network Security

✍ Scribed by J Anitha Ruth; Vijayalakshmi G V Mahesh; P Visalakshi

• Tongue: English
• Leaves: 557
• Category: Library

✦ Synopsis

In the relentless battle against escalating cyber threats, data security faces a critical challenge – the need for innovative solutions to fortify encryption and decryption processes. The increasing frequency and complexity of cyber-attacks demand a dynamic approach, and this is where the intersection of cryptography and machine learning emerges as a powerful ally. As hackers become more adept at exploiting vulnerabilities, the book stands as a beacon of insight, addressing the urgent need to leverage machine learning techniques in cryptography. Machine Learning and Cryptographic Solutions for Data Protection and Network Security unveil the intricate relationship between data security and machine learning and provide a roadmap for implementing these cutting-edge techniques in the field. The book equips specialists, academics, and students in cryptography, machine learning, and network security with the tools to enhance encryption and decryption procedures by offering theoretical frameworks and the latest empirical research findings. Its pages unfold a narrative of collaboration and cross-pollination of ideas, showcasing how machine learning can be harnessed to sift through vast datasets, identify network weak points, and predict future cyber threats.

✦ Table of Contents

Title Page
Copyright Page
Book Series
Table of Contents
Detailed Table of Contents
Preface
Chapter 1: A Survey of Cryptographic Data Protection and Machine Learning
Chapter 2: Innovative Machine Learning Applications for Cryptography
Chapter 3: Leveraging Artificial Intelligence for Cybersecurity
Chapter 4: Utilizations of AI in Cryptography
Chapter 5: Investigation of Machine Learning Approaches on Security Analysis of Cryptographic Algorithms
Chapter 6: Neuro-Inspired Algorithms to Enhance Cryptography
Chapter 7: Improving Ransomware Detection Using Machine Learning Algorithms
Chapter 8: Applications of Machine Learning Algorithms in Data Encryption Standards
Chapter 9: The Growth and Development of Data Security Using Cryptography Algorithms and Machine Learning Algorithms
Chapter 10: Next-Gen Cryptography
Chapter 11: Fortifying Machine Learning, Data Privacy, and Secure Collaboration
Chapter 12: Neural Network Approach of Combating the Data Security Issues
Chapter 13: Machine Learning Applications in Behavioural Authentication Data Using Gait Analysis
Chapter 14: Stochastic Quantum Bolt-Belief Neural Network-Based Insrusion Detection With Two-Fish Digital Hash Cryptography for Data Security
Chapter 15: Secure Data Transmission in the WSN Sector Utilizing a Heuristic Multi-Level Clustering Mechanism With Dynamic Trust Computation
Chapter 16: Unlocking Biologica Insights
Chapter 17: Machine Learning to Enhance Network Security
Chapter 18: Enhancing Password Security With Machine Learning-Based Strength Assessment Techniques
Chapter 19: Randomized Round Crypto Security Encryption Standard for Secure Cloud Storage
Chapter 20: Effective Implementation of the Database Primitive Functions Through Homomorphic Encryption Over Cloud
Chapter 21: A Smart Anomaly Detection Method in Cyber Physical Systems Using Machine Learning
Chapter 22: Intrusion Detection System in Mobile Networks
Chapter 23: Implementing Intelligent Encryption Using Machine Learning for Digital Information Real-Time Images
Chapter 24: An Adaptive-Selective Image Encryption With JSMP Map and Square-Wave Shuffling
Chapter 25: An Empirical Study and Analysis of Artificial Intelligence, Machine Learning, and Big Data for Crypto Healthcare Industries
Chapter 26: Supply Chain Governance Using DAO
Chapter 27: Forging an Ethical Paradigm With Moral Integrity in the Realm of Crypto Currency Investment
Compilation of References
About the Contributors
Index