Synopsis
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. Innovative Machine Learning Applications for Cryptography bridges the gap between two powerful domains and assists in diminishing the influence of human error on encryption and decryption processes. For academic scholars, engineers, scientists, and students, this book offers a valuable treasure trove of knowledge and actionable strategies in an age where the security of every byte is of utmost importance.
About the Author
Vijayalakshmi G V Mahesh received her BE in Electronics and Communication Engineering from Bangalore University, India in 1999, and M.Tech in Digital Communication and Networking from Visvesvaraya Technological University in 2005 and the Ph.D. degree from the Vellore Institute of Technology, Vellore, India. Currently she is working as an Associate Professor at BMS Institute of Technology and Management, Bangalore, India. She has been in academics for over 19 years and has published her research in various reputed journals and conferences. Her research interests include Machine Learning, Image Processing, Pattern Recognition and Deep learning, Affective computing. She has Memberships in Professional Bodies such as ISTE and IEI. P. Visalakshi is an Associate Professor at SRM Institute of Science and Technology based in Dist, Tamil Nadu.
R. Uma is working as Associate Professor in the Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, India. with 23 years of teaching experience for undergraduate and post graduate students in Computer Science Department. She has a Doctorate degree in Computer Science and Engineering from Anna University Chennai, India. She also completed her Master Degree in Computer Science and Engineering from College of Engineering Guindy, Anna University, India and Bachelor Degree from university of Madras. She has published her papers over 14 International Journals. She is a member of CSI, IACSIT and life member of ISTE. Her current research interest includes Information Retrieval, Data Mining, Deep learning and Machine learning .
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