GENETIC ALGORITHM OPTIMIZATION OF NEURAL NETWORK HYPERPARAMETERS FOR PREDICTING KEY BITS IN THE S-AES CIPHER - Boykuziev Ilkhom Mardanokulovich (The latest news and research in education, 2025)
Recent advances in machine learning have opened new directions in the cryptanalysis of lightweight block ciphers, particularly in the study of nonlinear components and key-dependent transformations. Building on prior work involving simplified cryptographic models such as Mini-AES and deep-learning-based attacks on lightweight ciphers, this study investigates the learnability of round-key bits in the Simplified Advanced Encryption Standard (S-AES). A structured dataset was generated by producing random 16-bit master keys and deriving their corresponding 48-bit subkey representations through the key-schedule algorithm. Additionally, two fixed plaintext blocks were encrypted under each key to construct three distinct training sets for the classification of the KPK_PKP​, KFK_FKF​, and KSK_SKS​ round-key bits. To examine the predictive potential of machine-learning models, Support Vector Machines (SVMs) were chosen as primary classifiers due to their robustness and proven ability to capture nonlinear decision boundaries even in limited training regimes. The Ray Tune optimization framework was employed to identify optimal SVM hyperparameters, leveraging distributed search mechanisms that have demonstrated superior performance compared with conventional optimizers such as HyperOpt and SMAC.