Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Ahmadzadeh, Ezat | - |
dc.contributor.author | Kim, Hyunil | - |
dc.contributor.author | Jeong, Ongee | - |
dc.contributor.author | Kim, Namki | - |
dc.contributor.author | Moon, Inkyu | - |
dc.date.accessioned | 2022-01-26T01:00:09Z | - |
dc.date.available | 2022-01-26T01:00:09Z | - |
dc.date.created | 2022-01-20 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/16160 | - |
dc.description.abstract | Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are a class of Recurrent Neural Networks (RNN) suitable for sequential data processing. Bidirectional LSTM (BLSTM) enables a better understanding of context by learning the future time steps in a bidirectional manner. Moreover, GRU deploys reset and update gates in the hidden layer, which is computationally more efficient than a conventional LSTM. This paper proposes an efficient network model based on deep BLSTM-GRU for ciphertext classification aiming to mark the category to which the ciphertext belongs. The proposed model performance was evaluated using well-known evaluation metrics on two publicly available datasets encrypted with various classical cipher methods and performance was compared against one-dimensional convolutional neural network (1D-CNN) and various other deep learning-based approaches. The experimental results showed that the BLSTM-GRU cell unit network model achieved a high classification accuracy of up to 95.8%. To the best of our knowledge, this is the first time an RNN-based model has been applied for the ciphertext classification. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2022.3140342 | - |
dc.identifier.scopusid | 2-s2.0-85122584854 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.10, pp.3228 - 3237 | - |
dc.description.isOpenAccess | TRUE | - |
dc.subject.keywordAuthor | Logic gates | - |
dc.subject.keywordAuthor | Ciphers | - |
dc.subject.keywordAuthor | Recurrent neural networks | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Encryption | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | bidirectional long short-term memory | - |
dc.subject.keywordAuthor | gated recurrent unit | - |
dc.subject.keywordAuthor | ciphertext classification | - |
dc.subject.keywordAuthor | 1D-convolutional neural networks | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | ATTACK | - |
dc.citation.endPage | 3237 | - |
dc.citation.startPage | 3228 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 10 | - |
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