Innovative Machine Learning Applications for CryptographyКНИГИ » ПРОГРАММИНГ
Название: Innovative Machine Learning Applications for Cryptography Автор: 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.
Preface Chapter 1: Introduction to Modern Cryptography and Machine Learning Chapter 2: Future Outlook Chapter 3: Artificial Intelligence-Supported Bio-Cryptography Protection Chapter 4: An Adaptive Cryptography Using OpenAI API Chapter 5: Optimized Deep Learning-Based Intrusion Detection Using WOA With LightGBM Chapter 6: A Survey of Machine Learning and Cryptography Algorithms Chapter 7: Quantum Cryptography Chapter 8: Minimizing Data Loss by Encrypting Brake-Light Images and Avoiding Rear-End Collisions Using Artificial Neural Network Chapter 9: Machine Learning Techniques to Predict the Inputs in Symmetric Encryption Algorithm Chapter 10: Homomorphic Encryption and Machine Learning in the Encrypted Domain Chapter 11: An Effective Combination of Pattern Recognition and Encryption Scheme for Biometric Authentication Systems Chapter 12: Enhancing Crypto Ransomware Detection Through Network Analysis and Machine Learning Chapter 13: A Survey of Innovative Machine Learning Approaches in Smart City Applications Chapter 14: Securing the IoT System of Smart Cities by Interactive Layered Neuro-Fuzzy Inference Network Classifier With Asymmetric Cryptography Compilation of References
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