ChaCha20 Encryption Algorithm Security Enhancement through Artificial Intelligence-Based Random Noisy Injection: A Case Study - Rangel Lugo, Edgar; Rangel Ríos , Kevin Uriel; González Vidales, Leonel; Bernal Beltrán , Carlos Alberto; Rangel Ríos, Cinthya Maybeth; Reynoso Andrés, Rosa Isabel; Rodríguez Torres, César del Ángel; Ascencio Antúnez, Lucero de Jesús (Cultura Científica y Tecnológica, 2025)
The problem of digital data theft is receiving growing attention in organizations because it may produce significant financial losses. This issue can be handled using dynamic encryption methodologies. There exists safety encryption alternatives such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). However, it is known that these algorithms have been threatened by quantum computing advent. Thereby, the aim of this research is to suggest novel dynamic encryption alternatives using artificial intelligence (AI), based on a noisy injection scheme on ciphertext, as it has the potential to mislead cybercriminals. Several aspects related to this subject were studied. Despite that quantum computing was not used, other measures have been proposed. The designed methodology was focused over the updating of ChaCha20 strategy combined with random Caesar II methodology. This fusion of techniques, referred to as random noisy ChaCha20, is suggested for increasing ciphertext security. Our novel proposal was compared with other random noisy alternatives such as random noisy DES, random noisy 3DES, random noisy AES-256, and random noisy Blowfish. The obtained results were dynamic ciphertext outputs. These schemes are limited to the ASCII table values. In conclusion, the suggested alternatives presented here may be difficult for cybercriminals to decrypt.