Machine learning for cryptanalysis

• Main Author: Yang, Allen Siwei
• Other Authors: Thomas Peyrin
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Science::Mathematics::Discrete mathematics::Cryptography; Science::Mathematics::Applied mathematics::Quantitative methods
• Online Access: https://hdl.handle.net/10356/156805
• Institution: Nanyang Technological University

Lightweight cryptography involves cryptography subject to constraints such as area and power consumption. In 2019, NIST organised a currently ongoing competition for lightweight authenticated encryption. This competition, currently in its final stage, has narrowed down the initial 57 submissions to 10 finalists, of which includes the cipher known as ASCON. Concurrent with this was a research paper published by Gohr in 2019. Here, it was shown that it was possible to apply deep learning to cryptanalysis. More specifically, it was possible to design a neural distinguisher for the Speck 32/64 cipher, where for a specified input difference, it was possible to distinguish between ciphertext pairs that had this input difference and a random one via classification. In this study, we provide an amalgamation of these two advances. Here, we first attempt to apply Gohr’s neural network architecture to the round function of the ASCON cipher. Following this, we attempt to improve on the architecture in hopes of greater accuracy. The primary result found was that Gohr’s network architecture had the ability to learn well up to 3.5 rounds of the ASCON round function. Following this, we were able to provide modifications for marginal improvement in accuracy of the 4 round case. Keywords - cryptanalysis, deep learning, lightweight cryptography