Название: Innovative Machine Learning Applications for CryptographyАвтор: J. Anitha Ruth, G.V. Vijayalakshmi, P. Visalakshi
Издательство: IGI Global
Год: 2024
Страниц: 313
Язык: английский
Формат: pdf (true), epub
Размер: 30.9 MB
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.
Machine Learning stands at the forefront of technological advancements, offering unparalleled capabilities to analyze data trends and fortify the security of encryption and decryption systems. This reference book delves into the symbiotic relationship between Machine Learning, data analysis, and the intricate domain of cryptography.
Machine Learning's prowess in constructing analytical models, automating processes, and adapting to vast datasets transforms the landscape of encryption and decryption. The association of Machine Learning approaches, such as boosting and mutual learning, with cryptosystems enables the generation of private cryptographic keys over public and potentially vulnerable channels. The inherent characteristics of Machine Learning approaches pave the way for the development of safer and more effective encryption and decryption methods, potentially mitigating the impact of human errors that could compromise organizational security.
The primary objective of this comprehensive reference book is to provide an extensive overview of recent theoretical and empirical work at the intersection of Machine Learning and cryptography. Readers will find relevant theoretical frameworks and the latest empirical research findings, shedding light on how Machine Learning can bolster encryption and decryption procedures by identifying and addressing data patterns that may expose vulnerabilities.
The thematic exploration spans various crucial topics within the field, including Encryption, Algorithm, Security, Elliptic Curve Cryptography, Cryptanalysis, Pairing-based Cryptography, Artificial Intelligence, Machine Learning, Authentication, Stream Cipher, Message Authentication, Homomorphism Encryption, Digital Signature Algorithm, Network Security, Quantum Cryptography, Biological Cryptography, and Neural Cryptography.
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