A Case Study of the Salsa20 Encryption Algorithm Using Random Noisy Injection Enhanced by Artificial Intelligence - Rangel Lugo, Edgar (2025)
Summary The loss of digital data can have severe financial consequences for organisations. This situation can handle with a dynamic encryption approach, which it can generate multiple ciphertexts from a single plaintext. This paper delves into the intersection of artificial intelligence, random noisy schemes, and cybersecurity, analyzing several critical aspects of this emerging field. The application of random noisy scheme to ciphertext, it has been suggested as a means of enhancing cybersecurity, with recent studies indicating that this approach can be effective against cybercriminals. The objective of this research was focused on applying random noisy strategy on ciphertext of the Salsa20 encryption algorithm, as this area remains unexplored. Given the promising results of random noisy strategies, organisations that they employ Salsa20 may benefit from this approach. This research is important because recent experiments have revealed the effectiveness of using artificial intelligence for noisy injection on ciphertext. Therefore, is here presented a new opportunity for organisations regarding the employment of the novel random noisy Salsa20 encryption alternative because the experimental results have shown an increasing of the dynamic performance on ciphertext. This work introduces the random noisy Salsa20 strategy as a novel dynamic encryption alternative, and comparison of the performance to four random noisy schemes based on DES, 3DES, AES-256, and Blowfish algorithms. In conclusion, the novel alternative that it is here recommended, it can be difficult for the cybercriminals to decrypt. Keywords: Applications of AI, cryptography, dynamic encryption methods, noisy injection strategies.