A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering
Keywords:cipher type detection, classical ciphers, feature engineering, neural networks, machine learning, Feedforward Neural Network (FFNN) classifier, Random Forest (RF) classifier, Naïve Bayes Network (NBN) classifier
AbstractCryptanalysis 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 A large number of encrypted historical documents exists, whose decryption can potentially increase the knowledge of historical events.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)”.
Copyright (c) 2021 Ernst Leierzopf, Nils Kopal, Bernhard Esslinger, Harald Lampesberger, Eckehard Hermann
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