In recent years, neural networks and cryptographic schemes have come together in war and
peace; a cross-impact that forms a dichotomy deserving a comprehensive review study …

Privacy-preserving deep neural networks have become essential and have attracted the
attention of many researchers due to the need to maintain the privacy and the confidentiality …

The construction and development of new techniques for a nonlinear multivalued Boolean
function is one of the important aspects of modern ciphers. These multivalued Boolean …

Machine learning has recently started to gain the attention of cryptographic researchers,
notably in block cipher cryptanalysis. Most of these machine learning-based approaches are …

One of the main security requirements for symmetric-key block ciphers is resistance against
differential cryptanalysis. This is commonly assessed by counting the number of active …

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption
Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified …

In this paper, we propose deep learning-based output prediction attacks in a blackbox
setting. As preliminary experiments, we first focus on two toy SPN block ciphers (small …

Neural cryptanalysis is the utilization of deep learning to attack cryptographic primitives. As
computing power increases, deploying neural cryptanalysis becomes a more feasible option …

In recent years, NNs, as the basic components in AI models and cryptographic schemes,
have come together in war and peace; a cross-impact forms a dichotomy deserving a …

Cryptanalysis in a blackbox setting using deep learning is powerful because it does not
require the attacker to have knowledge about the internal structure of the cryptographic …