CRYPTOANALYSIS OF THE ADVANCED ENCRYPTION STANDARD (AES) FOR PLAINTEXT RESTORATION USING SEQUENTIAL NEURAL NETWORKS
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Date
2023
Authors
Mussa, Daniyar
Journal Title
Journal ISSN
Volume Title
Publisher
School of Engineering and Digital Sciences
Abstract
In this thesis, we investigate the potential of deep neural networks for cryptanalysis,
with the aim of developing a network that can decipher encrypted data. Specifically,
we use long short-term memory networks and a convolutional neural network
to attempt to decrypt AES-encrypted plaint text and MNIST images, respectively.
We find that while the network is unable to fully decipher encrypted data, it can
successfully perform classification tasks on encrypted data. We also observe that the
choice of encryption key has an impact on the network’s performance. Our findings
suggest avenues for further research into the application of deep neural networks for
cryptanalysis.
Description
Keywords
Type of access: Restricted, Advanced Encryption Standard, Sequential Neural Networks, Plaintext Restoration
Citation
Mussa, D. (2023). Cryptoanalysis of the Advanced Encryption Standard (AES) for Plaintext Restoration using Sequential Neural Networks. School of Engineering and Digital Sciences