New results on machine learning-based distinguishers
Machine Learning (ML) is almost ubiquitously used in multiple disciplines nowadays. Recently, we have seen its usage in the realm of differential distinguishers for symmetric key ciphers. It has been shown that ML-based differential distinguishers can be easily extended to break round-reduced versio...
| Main Authors: | , , , , , |
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| 其他作者: | |
| 格式: | Journal Article |
| 语言: | English |
| 出版: |
2023
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| 主题: | |
| 在线阅读: | https://hdl.handle.net/10356/169249 |
| _version_ | 1826124472410701824 |
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| author | Baksi, Anubhab Breier, Jakub Dasu, Vishnu Asutosh Hou, Xiaolu Kim, Hyunji Seo, Hwajeong |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Baksi, Anubhab Breier, Jakub Dasu, Vishnu Asutosh Hou, Xiaolu Kim, Hyunji Seo, Hwajeong |
| author_sort | Baksi, Anubhab |
| collection | NTU |
| description | Machine Learning (ML) is almost ubiquitously used in multiple disciplines nowadays. Recently, we have seen its usage in the realm of differential distinguishers for symmetric key ciphers. It has been shown that ML-based differential distinguishers can be easily extended to break round-reduced versions of ciphers. In this paper, we show new distinguishers on the unkeyed and round-reduced versions of SPECK-32, SPECK-128, ASCON, SIMECK-32, SIMECK-64, and SKINNY-128. We explore multiple avenues in the process. In summary, we use neural networks and support vector machines in various settings (such as varying the activation function), apart from experimenting with a number of input difference tuples. Among other results, we show a distinguisher of 8-round SPECK-32 that works with low data complexity. |
| first_indexed | 2024-10-01T06:21:13Z |
| format | Journal Article |
| id | ntu-10356/169249 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:21:13Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1692492023-07-14T15:35:53Z New results on machine learning-based distinguishers Baksi, Anubhab Breier, Jakub Dasu, Vishnu Asutosh Hou, Xiaolu Kim, Hyunji Seo, Hwajeong School of Computer Science and Engineering Engineering::Computer science and engineering Speck Ascon Machine Learning (ML) is almost ubiquitously used in multiple disciplines nowadays. Recently, we have seen its usage in the realm of differential distinguishers for symmetric key ciphers. It has been shown that ML-based differential distinguishers can be easily extended to break round-reduced versions of ciphers. In this paper, we show new distinguishers on the unkeyed and round-reduced versions of SPECK-32, SPECK-128, ASCON, SIMECK-32, SIMECK-64, and SKINNY-128. We explore multiple avenues in the process. In summary, we use neural networks and support vector machines in various settings (such as varying the activation function), apart from experimenting with a number of input difference tuples. Among other results, we show a distinguisher of 8-round SPECK-32 that works with low data complexity. Published version This work was supported in part by the ‘‘University Silicon Austria Laboratories (SAL)’’ Initiative of SAL and its Austrian Partner Universities for Applied Fundamental Research for Electronic Based Systems, and in part by the Slovak Research and Development Agency under Contract SK-SRB-21-0059. 2023-07-10T05:57:59Z 2023-07-10T05:57:59Z 2023 Journal Article Baksi, A., Breier, J., Dasu, V. A., Hou, X., Kim, H. & Seo, H. (2023). New results on machine learning-based distinguishers. IEEE Access, 11, 54175-54187. https://dx.doi.org/10.1109/ACCESS.2023.3270396 2169-3536 https://hdl.handle.net/10356/169249 10.1109/ACCESS.2023.3270396 2-s2.0-85159662417 11 54175 54187 en IEEE Access © 2023 The authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Speck Ascon Baksi, Anubhab Breier, Jakub Dasu, Vishnu Asutosh Hou, Xiaolu Kim, Hyunji Seo, Hwajeong New results on machine learning-based distinguishers |
| title | New results on machine learning-based distinguishers |
| title_full | New results on machine learning-based distinguishers |
| title_fullStr | New results on machine learning-based distinguishers |
| title_full_unstemmed | New results on machine learning-based distinguishers |
| title_short | New results on machine learning-based distinguishers |
| title_sort | new results on machine learning based distinguishers |
| topic | Engineering::Computer science and engineering Speck Ascon |
| url | https://hdl.handle.net/10356/169249 |
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