Author: Dr. S. Brindha

ISBN: 978-81-69857-63-5

DOI: https://doi.org/10.59646/814

Date of Publication: September 04, 2026

Cite this book: Brindha S, (2026), Cryptography and Network Security using Text Encryption and Decryption for Deep Learning Methods, San International Scientific Publications, ISBN: 978-81-69857-63-5, DOI: https://doi.org/10.59646/814

Preface

The rapid expansion of digital communication, cloud computing, intelligent applications, and data-driven technologies has made information security an essential component of modern computing systems. As sensitive information is increasingly transmitted and stored through interconnected networks, protecting data against unauthorized access, interception, modification, and disclosure has become a significant technological challenge. Cryptography provides the fundamental mechanisms for securing digital information through encryption, decryption, authentication, confidentiality, and integrity. At the same time, the emergence of deep learning and intelligent computational methods has opened new possibilities for developing adaptive, efficient, and robust approaches to cryptographic data protection. The book Cryptography and Network Security using Text Encryption and Decryption for Deep Learning Methods presents an integrated exploration of these developments, with particular emphasis on intelligent encryption and decryption techniques.

The book begins with the fundamental concepts of cryptography, encryption, decryption, and commonly used cryptographic algorithms, establishing the theoretical foundation required for understanding advanced security mechanisms. It then introduces deep learning-based approaches for text cryptography and examines how intelligent computational models can contribute to the secure transformation and recovery of information. Particular attention is given to the development and evaluation of advanced cryptographic methods that combine machine learning techniques with established principles of information security.

The subsequent chapters present several advanced approaches, including the proposed Advanced Light Gradient Boosting Machine Text Cryptography Method (ALGBM-TCM) and the Enhanced Elliptic Curve Text Cryptography Method (EEC-TCM). These methods illustrate how efficient learning algorithms and public-key cryptographic principles can be integrated to improve the security and performance of text encryption and decryption. The book further explores digital optical chaotic mapping and chaos-based image cryptography, highlighting the potential of nonlinear and highly sensitive chaotic systems for protecting digital information. Preprocessing procedures, proposed workflows, algorithms, encryption mechanisms, and performance evaluation are discussed systematically to provide a comprehensive understanding of the methodologies.

An important objective of this book is to connect theoretical cryptographic principles with computationally oriented security solutions. The performance evaluation of the proposed methods provides a basis for examining their effectiveness and identifying their potential advantages in terms of security, computational efficiency, and reliable information protection. The discussions are intended to support researchers, postgraduate students, academicians, and professionals working in cryptography, network security, artificial intelligence, deep learning, and intelligent computing.