Machine Learning Power Side-Channel Attack on SNOW-V
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918263877795840 |
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| author | Deepak Balout, Rahul Golder, Anupam Kundu, Suparna Karmakar, Angshuman Das, Debayan |
| author_facet | Deepak Balout, Rahul Golder, Anupam Kundu, Suparna Karmakar, Angshuman Das, Debayan |
| contents | This paper demonstrates a power analysis-based Side-Channel Analysis (SCA) attack on the SNOW-V encryption algorithm, which is a 5G mobile communication security standard candidate. Implemented on an STM32 microcontroller, power traces captured with a ChipWhisperer board were analyzed, with Test Vector Leakage Assessment (TVLA) confirming exploitable leakage. Profiling attacks using Linear Discriminant Analysis (LDA) and Fully Connected Neural Networks (FCN) achieved efficient key recovery, with FCN achieving > 5X lower minimum traces to disclosure (MTD) compared to the state-of-the-art Correlational Power Analysis (CPA) assisted with LDA. The results highlight the vulnerability of SNOW-V to machine learning-based SCA and the need for robust countermeasures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21737 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Power Side-Channel Attack on SNOW-V Deepak Balout, Rahul Golder, Anupam Kundu, Suparna Karmakar, Angshuman Das, Debayan Cryptography and Security This paper demonstrates a power analysis-based Side-Channel Analysis (SCA) attack on the SNOW-V encryption algorithm, which is a 5G mobile communication security standard candidate. Implemented on an STM32 microcontroller, power traces captured with a ChipWhisperer board were analyzed, with Test Vector Leakage Assessment (TVLA) confirming exploitable leakage. Profiling attacks using Linear Discriminant Analysis (LDA) and Fully Connected Neural Networks (FCN) achieved efficient key recovery, with FCN achieving > 5X lower minimum traces to disclosure (MTD) compared to the state-of-the-art Correlational Power Analysis (CPA) assisted with LDA. The results highlight the vulnerability of SNOW-V to machine learning-based SCA and the need for robust countermeasures. |
| title | Machine Learning Power Side-Channel Attack on SNOW-V |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2512.21737 |