Secure and Resilient Authenticated Encryption Approach Based on Chaotic Neural Networks and Duplex Construction | Chapter 8 | Advances and Challenges in Science and Technology Vol. 6

 This episode delves deep into the intricate interaction of procedures that mark the foundations of Authenticated Encryption (AE) and its importance in preserving the secrecy and authenticity of our mathematical communications. As the mathematical age progresses, telecommunication systems have shifted towards mathematical paradigms, compelled not only by the affordability and approachability of digital parts but also by the basic benefits they bring. Herein, we present a distinct approach from the Standard Duplex Construction (SDC) popular as the Modified Duplex Construction (MDC). The MDC incorporates two important phases: the initialization chapter and the duplexing phase, each encompassing a Chaotic Neural Network Revised (CNNR) delineated by a singular-hide neural form enriched accompanying non-linear functionalities. The chapter further confers the implementation of MDC in two distinguishing widths of 512 and 1024 bits. A exact evaluation concerning this construction against various cryptanalytic warnings showcases its elasticity and robustness. In this discourse, lectors will encounter the development, achievement, and analysis of a novel Authenticated Encryption with Associated Data Scheme (AEADS), created from the chaotic worlds of neural networks. The episode explicates the encryption and decryption processes of AEADS, stressing the crucial duty of variables such as IV, K, AD, and M in encryption, and C and T in decryption. The dependability of the decryption is liable to be subjected the alignment of computed and taken tags, dictating either the explanation of the original message or the generation of an wrong. The decryption complications, encompassing variables like C, T, IV, K, and AD, are still elucidated. Two distinct processes have existed instituted for message lengths connecting 64 and 128 bytes, providing a comprehensive view of the blueprint’s versatility.

Author(s) Details:

Nabil Abdoun,
School of Arts and Sciences, Lebanese American University, Beirut, Lebanon.

Safwan El Assad,
Nantes Universit ´ e, CNRS, IETR, UMR 6164, F-44000 Nantes, France.

Thang Manh Hoang,
Vietnam-Japan International Institute for Science of Technology, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi 10999, Vietnam.

Olivier Deforges,
INSA de Rennes, CNRS, IETR, UMR 6164, F-35708 Rennes, France.

Rima Assaf,
Faculty of Engineering, Lebanese University, Beirut, Lebanon.

Mohamad Khalil,
Faculty of Engineering, Lebanese University, Beirut, Lebanon.

Please see the link here: https://stm.bookpi.org/ACST-V6/article/view/12135