In recent years, machine learning techniques have been successfully applied to improve side-channel attacks against different cryptographic algorithms. In this work, we deal with the use of neural networks to attack elliptic curve-based cryptosystems. In particular, we propose a deep learning based strategy to retrieve the scalar from a double-and-add scalar-point multiplication. As a proof of concept, we conduct an effective attack against the scalar-point multiplication on NIST standard curve P-256 implemented in BearSSL, a timing side-channel hardened public library. The experimental results show that our attack strategy allows to recover the secret scalar value with a single trace from the attacked device and an exhaustive search over a set containing a few hundreds of the sought secret.

Profiled Attacks Against the Elliptic Curve Scalar Point Multiplication Using Neural Networks

Barenghi A.;Pelosi G.;
2021

Abstract

In recent years, machine learning techniques have been successfully applied to improve side-channel attacks against different cryptographic algorithms. In this work, we deal with the use of neural networks to attack elliptic curve-based cryptosystems. In particular, we propose a deep learning based strategy to retrieve the scalar from a double-and-add scalar-point multiplication. As a proof of concept, we conduct an effective attack against the scalar-point multiplication on NIST standard curve P-256 implemented in BearSSL, a timing side-channel hardened public library. The experimental results show that our attack strategy allows to recover the secret scalar value with a single trace from the attacked device and an exhaustive search over a set containing a few hundreds of the sought secret.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
978-3-030-92707-3
978-3-030-92708-0
Applied cryptography
Computer security
Elliptic curve cryptography
Neural networks
Profiled side channel attacks
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1198513
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