Unveiling ECC Vulnerabilities: LSTM Networks for Operation Recognition in Side-Channel Attacks
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arXiv
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| Format: | Preprint |
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2025
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| author | Battistello, Alberto Bertoni, Guido Corrias, Michele Nava, Lorenzo Rusconi, Davide Zoia, Matteo Pierazzi, Fabio Lanzi, Andrea |
| author_facet | Battistello, Alberto Bertoni, Guido Corrias, Michele Nava, Lorenzo Rusconi, Davide Zoia, Matteo Pierazzi, Fabio Lanzi, Andrea |
| contents | We propose a novel approach for performing side-channel attacks on elliptic curve cryptography. Unlike previous approaches and inspired by the ``activity detection'' literature, we adopt a long-short-term memory (LSTM) neural network to analyze a power trace and identify patterns of operation in the scalar multiplication algorithm performed during an ECDSA signature, that allows us to recover bits of the ephemeral key, and thus retrieve the signer's private key. Our approach is based on the fact that modular reductions are conditionally performed by micro-ecc and depend on key bits.
We evaluated the feasibility and reproducibility of our attack through experiments in both simulated and real implementations. We demonstrate the effectiveness of our attack by implementing it on a real target device, an STM32F415 with the micro-ecc library, and successfully compromise it. Furthermore, we show that current countermeasures, specifically the coordinate randomization technique, are not sufficient to protect against side channels. Finally, we suggest other approaches that may be implemented to thwart our attack. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling ECC Vulnerabilities: LSTM Networks for Operation Recognition in Side-Channel Attacks Battistello, Alberto Bertoni, Guido Corrias, Michele Nava, Lorenzo Rusconi, Davide Zoia, Matteo Pierazzi, Fabio Lanzi, Andrea Cryptography and Security Machine Learning We propose a novel approach for performing side-channel attacks on elliptic curve cryptography. Unlike previous approaches and inspired by the ``activity detection'' literature, we adopt a long-short-term memory (LSTM) neural network to analyze a power trace and identify patterns of operation in the scalar multiplication algorithm performed during an ECDSA signature, that allows us to recover bits of the ephemeral key, and thus retrieve the signer's private key. Our approach is based on the fact that modular reductions are conditionally performed by micro-ecc and depend on key bits. We evaluated the feasibility and reproducibility of our attack through experiments in both simulated and real implementations. We demonstrate the effectiveness of our attack by implementing it on a real target device, an STM32F415 with the micro-ecc library, and successfully compromise it. Furthermore, we show that current countermeasures, specifically the coordinate randomization technique, are not sufficient to protect against side channels. Finally, we suggest other approaches that may be implemented to thwart our attack. |
| title | Unveiling ECC Vulnerabilities: LSTM Networks for Operation Recognition in Side-Channel Attacks |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2502.17330 |