Machine Learning and Cryptographic Solutions for Data Protection and Network Security

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. This book is an indispensable guide for scholars navigating the intricate domains of Elliptic Curve Cryptography, Cryptanalysis, Pairing-based Cryptography, Artificial Intelligence, Digital Signature Algorithms, and more. It not only sheds light on current challenges but also provides actionable insights and recommendations, making it an essential resource for those seeking to understand the evolving landscape of data security and actively contribute to its fortification. In a world where the stakes of cybersecurity are higher than ever, this book emerges as a beacon of knowledge, offering a proactive and informed solution to the persistent challenges faced by the research community.