Deep Learning-based Cryptanalysis of Different AES Modes of Operation
Date Issued
2022
Author(s)
Gjorgjievska Perusheska, Milena
Mihajloska Trpcheska, Hristina
Abstract
With the advent of machine learning and the development of
powerful machines, the problem of decryption takes on a new light and opens
new avenues for research. The remarkable rise of technologies and algorithms
contributes to the widespread use of machine learning in various cases. One of
the uses is in cryptanalysis and attack of algorithms used in cryptographic
processes. In this paper, we elaborate the idea by using the deep neural network
to perform the known-plaintext attack on AES to restore as many bits as
possible, on the given plaintext. Moreover, we perform our experiments on
different key sizes and different modes of operation on AES. The results show
that the deep neural network can restore the bits in the whole data set with a
probability of more than 98%, restore two consecutive bytes with more than
70%, and more than half of the plaintext bytes with a probability of 99%.
powerful machines, the problem of decryption takes on a new light and opens
new avenues for research. The remarkable rise of technologies and algorithms
contributes to the widespread use of machine learning in various cases. One of
the uses is in cryptanalysis and attack of algorithms used in cryptographic
processes. In this paper, we elaborate the idea by using the deep neural network
to perform the known-plaintext attack on AES to restore as many bits as
possible, on the given plaintext. Moreover, we perform our experiments on
different key sizes and different modes of operation on AES. The results show
that the deep neural network can restore the bits in the whole data set with a
probability of more than 98%, restore two consecutive bytes with more than
70%, and more than half of the plaintext bytes with a probability of 99%.
Subjects
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