Please use this identifier to cite or link to this item:
Title: Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives
Authors: Tu, Yi
Keywords: Science::Mathematics::Discrete mathematics::Cryptography
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Tu, Y. (2022). Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Information security has received more and more attentions in recent decades with the rapid developments of the internet era. Since symmetric cryptographic primitives are widely used in current information systems, doing cryptanalysis of symmetric cryptographic primitives to evaluate the security is becoming increasingly significant. This thesis focuses on the cryptanalysis of block ciphers and hash functions assisted by tools including automatic tools and machine learning techniques, and shows the advantages of machine learning-aided and SAT-aided cryptanalysis over pure classical cryptanalysis. Firstly, regarding Keccak-f is the permutation used in the NIST SHA-3 hash function standard, we introduce a classical algorithm to exhaustively search for 3-round trail cores of Keccak-f [1600]. Then we develop a SAT-based automatic search toolkit to obtain differential trails for Keccak-f. With the help of this tool, we present the first 6-round classical collision attack on SHAKE128. Besides, we explore using neural networks to assist classical cryptanalysis and present the first practical 13-round neural-distinguisher-based key-recovery attacks on Speck32/64, which is a lightweight block cipher designed by NSA.
DOI: 10.32657/10356/160785
Schools: School of Physical and Mathematical Sciences 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Theses

Files in This Item:
File Description SizeFormat 
Thesis_YiTu.pdf5.52 MBAdobe PDFThumbnail

Page view(s)

Updated on Dec 7, 2023

Download(s) 50

Updated on Dec 7, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.