# Unveiling ECC Vulnerabilities: LSTM Networks for Operation Recognition in Side-Channel Attacks

arXiv: https://arxiv.org/abs/2502.17330 · HTML: https://mlsanity.com/paper/2502.17330/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [SCAUL: Power Side-Channel Analysis with Unsupervised Learning](https://mlsanity.com/paper/2001.05951/) (2020)

## Built on by (most cited first, 1 of 1)

- [Mage: Cracking Elliptic Curve Cryptography with Cross-Axis Transformers](https://mlsanity.com/paper/2512.12483/) (2025)

## Abstract (arXiv)

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.