Cryptology ePrint Archive: Report 2020/891

Keep it Unsupervised: Horizontal Attacks Meet Deep Learning

Guilherme Perin and Lukasz Chmielewski and Lejla Batina and Stjepan Picek

Abstract: 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 secret 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 error bits from randomized scalars (or exponents). When a simple horizontal attack can recover around 52% of private key bits, the proposed iterative framework improves the private key correctness to 100%. 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.

Category / Keywords: applications / Side-channel Analysis, Public-key Algorithms, Horizontal Attacks, Deep Learning

Date: received 15 Jul 2020, last revised 16 Jul 2020

Contact author: guilhermeperin7 at gmail com, lukchmiel@gmail com, picek stjepan@gmail com, lejla@cs ru nl

Available format(s): PDF | BibTeX Citation

Version: 20200716:135108 (All versions of this report)

Short URL: ia.cr/2020/891


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