Abstract
This paper studies the use of deep learning (DL) models under a known-plaintext scenario. The goal of the models is to predict the secret key of a cipher using DL techniques. We investigate the DL techniques against different ciphers, namely, Simplified Data Encryption Standard (S-DES), Speck, Simeck and Katan. For S-DES, we examine the classification of the full key set, and the results are better than a random guess. However, we found that it is difficult to apply the same classification model beyond 2-round Speck. We also demonstrate that DL models trained under a known-plaintext scenario can successfully recover the random key of S-DES. However, the same method has been less successful when applied to modern ciphers Speck, Simeck, and Katan. The ciphers Simeck and Katan are further investigated using the DL models but with a text-based key. This application found the linear approximations between the plaintext–ciphertext pairs and the text-based key.
Highlights
Introduction of Cryptographic AlgorithmsCryptanalysis and machine learning, developed during World War 2, are closely linked [1]
We investigate the capability of the neural network against modern ciphers under a known-plaintext scenario
Instead of attacking the round reduced ciphers, they demonstrated a successful attack on the full-round lightweight block ciphers Simon and Speck, where the keyspace was restricted to a set of 64 ASCII characters
Summary
Neural networks are a subset of machine learning. A neural network is comprised of node layers: an input layer, one or more hidden layers, and an output layer [13]. If a neural network has multiple hidden layers, it is called a deep neural network. Artificial neuron, is linked to another and has its own weight and threshold. A node is activated and begins delivering data to the subsequent layer if the output of the node exceeds the set threshold value. Neural networks use the training data to learn and enhance their accuracy over time
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