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A Novel Dynamic Attack on Classical Ciphers Using an Attention-Based LSTM Encoder-Decoder Model

Title
A Novel Dynamic Attack on Classical Ciphers Using an Attention-Based LSTM Encoder-Decoder Model
Authors
Ahmadzadeh, EzatKim, HyunilJeong, OngeeMoon, Inkyu
DGIST Authors
Ahmadzadeh, Ezat; Kim, Hyunil; Jeong, Ongee; Moon, Inkyu
Issue Date
2021-04
Citation
IEEE Access, 9, 60960-60970
Type
Article
Author Keywords
CiphersLogic gatesCryptographyRecurrent neural networksDecodingComputer architectureFeature extractionCryptanalysisclassical ciphersattention-based LSTM encoder-decoderrecurrent neural network
Keywords
CryptographyDecodingDeep learningSecurity of dataSignal encodingAttention mechanismsCharacter levelComplexity levelsData-communicationPerformance comparisonSequence lengthsSequential processingState-of-the-art methodsLong short-term memory
ISSN
2169-3536
Abstract
Information security has become an intrinsic part of data communication. Cryptanalysis using deep learning–based methods to identify weaknesses in ciphers has not been thoroughly studied. Recently, long short-term memory (LSTM) networks have shown promising performance in sequential data processing by modeling the dependencies and data dynamics. Given an encrypted ciphertext sequence and corresponding plaintext, by taking advantage of sequential processing, LSTM can adaptively discover the decryption function regardless of the complexity level, which substantially outperforms traditional methods. However, a lengthy ciphertext sequence causes LSTM to lose important information along the sequence, leading to a decrease in network performance. To tackle these problems, we propose adding an attention mechanism to enhance the LSTM sequential processing power. This paper presents a novel, dynamic way to attack classical ciphers by using an attention-based LSTM encoder-decoder for different ciphertext sequence lengths. The proposed approach takes in a sequence of ciphertext and outputs a sequence of plaintext. The effectiveness and flexibility of the proposed model were evaluated on different classical ciphers. We got close to 100% accuracy in breaking all types of classical ciphers in character-level and word-level attacks. We empirically provide further insights into our results on two datasets with short and long ciphertext lengths. In addition, we provide a performance comparison of the proposed method against state-of-the-art methods. The proposed approach has the potential to attack modern ciphers. To the best of our knowledge, this is the first time an attention-based LSTM encoder-decoder has been applied to attack classical ciphers. CCBY
URI
http://hdl.handle.net/20.500.11750/13593
DOI
10.1109/ACCESS.2021.3074268
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Moon, Inkyu Intelligent Imaging and Vision Systems Laboratory
  • Research Interests
Files:
There are no files associated with this item.
Collection:
Department of Robotics and Mechatronics EngineeringIntelligent Imaging and Vision Systems Laboratory1. Journal Articles


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