Classifying Classic Ciphers using Machine Learning

Krishna, Nivedhitha Ramarathnam

doi:10.31979/etd.xkgs-5gy6

Cited by 3 publications

(3 citation statements)

References 11 publications

(17 reference statements)

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“…The greatest part of original historic encrypted manuscripts are only between a few lines and some pages of ciphertext long. Krishna (2019) developed approaches that have not yet been used by other authors for the four ciphers Simple Substitution, Vigenère, Transposition and Playfair. An important point for comparison is that the Hill cipher was not used here.…”

Section: Related Workmentioning

confidence: 99%

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

Leierzopf¹,

Kopal²,

Esslinger³

et al. 2021

Linköping Electronic Conference Proceedings

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Cryptanalysis of enciphered documents typically starts with identifying the cipher type. A large number of encrypted historical documents exists, whose decryption can potentially increase the knowledge of historical events. This paper investigates whether machine learning can support the cipher type classification task when only ciphertexts are given. A selection of engineered features for historical ciphertexts and various machine-learning classifiers have been applied for 56 different cipher types specified by the American Cryptogram Association. Different neuronal network models were empirically evaluated. Our best-performing model achieved an accuracy of 80.24% which improves the current state of the art by 37%. Accuracy is calculated by dividing the total number of samples by the number of true positive predictions. The software-suite is published under the name "Neural Cipher Identifier (NCID)".

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Section: Related Workmentioning

confidence: 99%

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

Leierzopf¹,

Kopal²,

Esslinger³

et al. 2021

Linköping Electronic Conference Proceedings

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“…This research focuses on using DL techniques to perform cryptanalysis on block ciphers in general. This investigation aims to diagnose and analyze the cryptography algorithms utilized to generate the ciphertext message [7].…”

Section: Introductionmentioning

confidence: 99%

Block Ciphers Analysis Based on a Fully Connected Neural Network

Alwan

Kashmar

2023

IHJPAS

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With the development of high-speed network technologies, there has been a recent rise in the transfer of significant amounts of sensitive data across the Internet and other open channels. The data will be encrypted using the same key for both Triple Data Encryption Standard (TDES) and Advanced Encryption Standard (AES), with block cipher modes called cipher Block Chaining (CBC) and Electronic CodeBook (ECB). Block ciphers are often used for secure data storage in fixed hard drives, portable devices, and safe network data transport. Therefore, to assess the security of the encryption method, it is necessary to become familiar with and evaluate the algorithms of cryptographic systems. Block cipher users need to be sure that the ciphers they employ are secure against various attacks. A Fully Connected Neural Network (FCNN) model was initially used to assess how well the models were classified. Then, all models, including encoder models, were assessed using True Positive (TP) measures for successful classification of the discovered encoder and False Positive (FP) measures for imprecise categorization. The accuracy value, retrieval, loss, precision value, and F1 score were then calculated using a confusion matrix to assess the model's efficacy (abbreviated as F1). ECB results with an accuracy of 85% and CBC results with an accuracy of 88% were produced, and the parameters of the FCNN model were tweaked to provide better results. These results helped to identify the encryption algorithm more precisely and evaluate it.

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“…Although these ciphers have all been broken [4,5,9,20], and cryptography has improved significantly since World War II, accurately classifying ciphertext generated by these machines is an interesting challenge. There has been previous research on classifying classic ciphers by applying machine learning techniques [13], but as far as the authors are aware, to date no such work has focused on the WWII-era ciphers considered in this paper.…”

Section: Introductionmentioning

confidence: 99%

Classifying World War II Era Ciphers with Machine Learning

Dalton

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We determine the accuracy with which machine learning and deep learning techniques can classify selected World War II era ciphers when only ciphertext is available. The specific ciphers considered are Enigma, M-209, Sigaba, Purple, and Typex. We experiment with three classic machine learning models, namely, Support Vector Machines (SVM), 𝑘-Nearest Neighbors (𝑘-NN), and Random Forest (RF). We also experiment with four deep learning neural network-based models: Multi-Layer Perceptrons (MLP), Long Short-Term Memory (LSTM), Extreme Learning Machines (ELM), and Convolutional Neural Networks (CNN). Each model is trained on features consisting of histograms, digrams, and raw ciphertext letter sequences. Furthermore, the classification problem is considered under four distinct scenarios: Fixed plaintext with fixed keys, random plaintext with fixed keys, fixed plaintext with random keys, and random plaintext with random keys. Under the most realistic scenario, given 1000 characters per ciphertext, we are able to distinguish the ciphers with greater than 97% accuracy. In addition, we consider the accuracy of a subset of the learning techniques as a function of the length of the ciphertext messages. Somewhat surprisingly, our classic machine learning models perform at least as well as our deep learning models. We also find that ciphers that are more similar in design are somewhat more challenging to distinguish, but not as difficult as might be expected.

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Classifying Classic Ciphers using Machine Learning

Abstract: ROC plots for SVM trained on different lengths of ciphertext. 24 CNN model training accuracy for 2 and 3 Con2D layers. .. .. 25 Confusion matrix plots for CNN having two Conv2D layers for as 2,4,5,7. . .

Cited by 3 publications

References 11 publications

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

Block Ciphers Analysis Based on a Fully Connected Neural Network

Classifying World War II Era Ciphers with Machine Learning

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