CRYPTOANALYSIS OF THE ADVANCED ENCRYPTION STANDARD (AES) FOR PLAINTEXT RESTORATION USING SEQUENTIAL NEURAL NETWORKS

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dc.contributor.authorMussa, Daniyar
dc.date.accessioned2023-06-12T09:27:03Z
dc.date.available2023-06-12T09:27:03Z
dc.date.issued2023
dc.description.abstractIn 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.en_US
dc.identifier.citationMussa, D. (2023). Cryptoanalysis of the Advanced Encryption Standard (AES) for Plaintext Restoration using Sequential Neural Networks. School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7213
dc.language.isoenen_US
dc.publisherSchool of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.subjectAdvanced Encryption Standarden_US
dc.subjectSequential Neural Networksen_US
dc.subjectPlaintext Restorationen_US
dc.titleCRYPTOANALYSIS OF THE ADVANCED ENCRYPTION STANDARD (AES) FOR PLAINTEXT RESTORATION USING SEQUENTIAL NEURAL NETWORKSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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