Symmetric-key encryption remains a cornerstone of modern cryptographic security, offering an efficient mechanism for securing digital communication. This study investigates the feasibility of classifying key bits of the Simplified Advanced Encryption Standard (S-AES) using machine learning techniques, particularly multilayer perceptron (MLP) neural networks. A dataset of plaintext–ciphertext pairs generated from random 16-bit encryption keys is used to train multiple neural models with varying hyperparameters. The results demonstrate that certain key bits exhibit higher learnability than others, suggesting non-uniform model sensitivity across the key space. The findings emphasize the importance of hyperparameter selection and highlight potential implications for cryptanalysis research.
Classification Of Symmetric Encryption Key Bits Using Artificial Neural Networks
DOI:
Keywords:
Symmetric encryption
S-AES
key bit classification
neural networks
Abstract
References
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