Название: Machine Learning and Cryptographic Solutions for Data Protection and Network SecurityАвтор: J. Anitha Ruth, Vijayalakshmi G.V. Mahesh, P. Visalakshi, R. Uma, A. Meenakshi
Издательство: IGI Global
Год: 2024
Страниц: 557
Язык: английский
Формат: pdf (true), epub
Размер: 46.2 MB
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.
In an era of information flooding, a data breach not only results in economic loss, but also a loss of goodwill for the business applications. Cryptographic algorithms have been studied and applied for the protection of sensitive data for a very long time. Machine learning applications deal with an enormous amount of data where the data may be critical and sensitive. There are a plethora of Machine Learning applications where cryptography is applied for the protection of data. In the same way, Machine Learning algorithms can be used for implementing cryptographic algorithms. It is used for analyzing and finding hidden patterns to improve the credibility of the security algorithms. The Chapter 1 analyzes the contribution of cryptography to Machine Learning algorithms and vice versa. It also describes the challenges and opportunities in application and interaction between these two fields.
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.
Coverage:
The many academic areas covered in this publication include, but are not limited to:
Artificial Intelligence
Biological Cryptography
Cryptanalysis
Digital Signature Algorithm
Elliptic Curve Cryptography
Machine Learning
Network Security
Neural Cryptography
Pairing-based Cryptography
Quantum Cryptography
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