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.
Highlights
With the increasing rate of data transfer over the internet, system security is becoming one of the most important issues for information exchange [1], [2]
The results showed that the proposed network model outperformed other deep learning-based approaches and was more efficient in automated ciphertext classification
This section first introduces datasets used to evaluate the efficiency of the proposed network model for ciphertext classification
Summary
With the increasing rate of data transfer over the internet, system security is becoming one of the most important issues for information exchange [1], [2]. Cryptanalysis investigates the weakness of the cryptosystem to ensure system security. The attacker tries to recover the original form of a secured message by analyzing hidden patterns in data or finding the secret key. One of the main ways to attack the cryptosystem is to analyze the hidden data patterns to reveal the main information of the ciphertext [1], [6], [7]. This is referred to as building a structured knowledge representation by extracting the features from the ciphertext normally using machine learning algorithms to process the information
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
