Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives
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...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/160785 |
| _version_ | 1824453774768340992 |
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| author | Tu, Yi |
| author2 | Guo Jian |
| author_facet | Guo Jian Tu, Yi |
| author_sort | Tu, Yi |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:11:46Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/160785 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:11:46Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1607852023-02-28T23:57:13Z Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives Tu, Yi Guo Jian School of Physical and Mathematical Sciences guojian@ntu.edu.sg Science::Mathematics::Discrete mathematics::Cryptography 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. Doctor of Philosophy 2022-08-03T00:55:38Z 2022-08-03T00:55:38Z 2022 Thesis-Doctor of Philosophy Tu, Y. (2022). Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/160785 https://hdl.handle.net/10356/160785 10.32657/10356/160785 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics::Discrete mathematics::Cryptography Tu, Yi Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives |
| title | Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives |
| title_full | Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives |
| title_fullStr | Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives |
| title_full_unstemmed | Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives |
| title_short | Machine learning-aided and SAT-aided cryptanalysis of symmetric-key primitives |
| title_sort | machine learning aided and sat aided cryptanalysis of symmetric key primitives |
| topic | Science::Mathematics::Discrete mathematics::Cryptography |
| url | https://hdl.handle.net/10356/160785 |
| work_keys_str_mv | AT tuyi machinelearningaidedandsataidedcryptanalysisofsymmetrickeyprimitives |