Keep it Unsupervised

Keep it Unsupervised: Horizontal Attacks Meet Deep Learning

Perin, G. (TU Delft Cyber Security)
Chmielewski, Łukasz (Riscure; Radboud Universiteit Nijmegen)
Batina, Lejla (Radboud Universiteit Nijmegen)
Picek, S. (TU Delft Cyber Security)

2020

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.

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

http://resolver.tudelft.nl/uuid:dcb41346-3884-47f7-932b-74e9f4d40593

https://doi.org/10.46586/tches.v2021.i1.343-372

2569-2925

IACR Transactions on Cryptographic Hardware and Embedded Systems, 2021 (1), 343-372

Institutional Repository

journal article

© 2020 G. Perin, Łukasz Chmielewski, Lejla Batina, S. Picek