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Extracting Information from Encrypted Data using Deep Neural Networks
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this paper we explore various approaches to using deep neural networks to per- form cryptanalysis, with the ultimate goal of having a deep neural network deci- pher encrypted data. We use long short-term memory networks to try to decipher encrypted text and we use a convolutional neural network to perform classification tasks on encrypted MNIST images. We find that although the network is unable to decipher encrypted data, it is able to perform classification on encrypted data. We also find that the networks performance is depending on what key were used to en- crypt the data. These findings could be valuable for further research into the topic of cryptanalysis using deep neural networks.

Place, publisher, year, edition, pages
2018. , p. 52
Keywords [en]
Neuralnetworks, MachineLearning, Cryptography, DES, LSTM, CNN, Cryptanalysis
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:umu:diva-155904OAI: oai:DiVA.org:umu-155904DiVA, id: diva2:1284274
External cooperation
Omnegapoint AB
Educational program
Master of Science Programme in Interaction Technology and Design - Engineering
Supervisors
Examiners
Available from: 2019-02-04 Created: 2019-01-31 Last updated: 2019-02-04Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf