Keep it Unsupervised: Horizontal Attacks Meet Deep Learning


  • Guilherme Perin Delft University of Technology, The Netherlands
  • Łukasz Chmielewski Radboud University Nijmegen, The Netherlands; Riscure BV, The Netherlands
  • Lejla Batina Radboud University Nijmegen, The Netherlands
  • Stjepan Picek Delft University of Technology, The Netherlands



Side-channel Analysis, Public-key Algorithms, Horizontal Attacks, Deep Learning


To mitigate side-channel attacks, real-world implementations of public-key cryptosystems adopt state-of-the-art countermeasures based on randomization of the private or ephemeral keys. Usually, for each private key operation, a “scalar blinding” is performed using 32 or 64 randomly generated bits. Nevertheless, horizontal attacks based on a single trace still pose serious threats to protected ECC or RSA implementations. If the secrets learned through a single-trace attack contain too many wrong (or noisy) bits, the cryptanalysis methods for recovering remaining bits become impractical due to time and computational constraints. This paper proposes a deep learning-based framework to iteratively correct partially correct private keys resulting from a clustering-based horizontal attack. By testing the trained network on scalar multiplication (or exponentiation) traces, we demonstrate that a deep neural network can significantly reduce the number of wrong bits from randomized scalars (or exponents).
When a simple horizontal attack can recover around 52% of attacked multiple private key bits, the proposed iterative framework improves the private key accuracy to above 90% on average and to 100% for at least one of the attacked keys. Our attack model remains fully unsupervised and excludes the need to know where the error or noisy bits are located in each separate randomized private key.




How to Cite

Perin, G., Chmielewski, Łukasz, Batina, L., & Picek, S. (2020). Keep it Unsupervised: Horizontal Attacks Meet Deep Learning. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2021(1), 343–372.