Cryptanalysis of Round-Reduced ASCON powered by ML
Date Issued
2022-05-05
Author(s)
Jankovikj, Dushica
Mihajloska Trpceska, Hristina
Abstract
Our research focuses on attacking Ascon, a
lightweight block cipher presented as a candidate in the NIST
Lightweight Cryptography Standardization Process. This block
cipher provides authenticated encryption with associated data
functionalities. We propose a cryptanalysis model based on deep
learning (DL), where the goal is to predict plaintext bits given
knowledge of the ciphertext and other publicly known cipher
input parameters. Our experiments show that such knownplaintext attacks can be successfully executed on a round
reduced version of the cipher stripped of the finalization phase.
This, in turn, validates the theoretical results. Cryptographic
algorithms are complex for the purpose of security and cannot
be easily broken by an ML model in their regular form (not
reduced). We explore multiple dataset generation techniques,
model design, and training hyperparameters.
lightweight block cipher presented as a candidate in the NIST
Lightweight Cryptography Standardization Process. This block
cipher provides authenticated encryption with associated data
functionalities. We propose a cryptanalysis model based on deep
learning (DL), where the goal is to predict plaintext bits given
knowledge of the ciphertext and other publicly known cipher
input parameters. Our experiments show that such knownplaintext attacks can be successfully executed on a round
reduced version of the cipher stripped of the finalization phase.
This, in turn, validates the theoretical results. Cryptographic
algorithms are complex for the purpose of security and cannot
be easily broken by an ML model in their regular form (not
reduced). We explore multiple dataset generation techniques,
model design, and training hyperparameters.
Subjects
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