Non-Profiled Unsupervised Horizontal Iterative Attack against Hardware Elliptic Curve Scalar Multiplication Using Machine Learning

Marcin Aftowicz, Ievgen Kabin, Zoya Dyka and Peter Langendörfer ()
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Marcin Aftowicz: Leibniz-Institut für Innovative Mikroelektronik—IHP, 15236 Frankfurt (Oder), Germany
Ievgen Kabin: Leibniz-Institut für Innovative Mikroelektronik—IHP, 15236 Frankfurt (Oder), Germany
Zoya Dyka: Leibniz-Institut für Innovative Mikroelektronik—IHP, 15236 Frankfurt (Oder), Germany
Peter Langendörfer: Leibniz-Institut für Innovative Mikroelektronik—IHP, 15236 Frankfurt (Oder), Germany

Future Internet, 2024, vol. 16, issue 2, 1-23

Abstract: While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption, or electromagnetic emanation, to compromise cryptographic operations and extract the secret key. This work presents a side channel analysis of a cryptographic hardware accelerator for the Elliptic Curve Scalar Multiplication operation, implemented in a Field-Programmable Gate Array and as an Application-Specific Integrated Circuit. The presented framework consists of initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take, as input, the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.

Keywords: side channel analysis; machine learning; horizontal attack; non-profiled attack; FPGA; ASIC (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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