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http://hdl.handle.net/1942/27465
Title: | Improving CEMA using Correlation Optimization | Authors: | ROBYNS, Pieter QUAX, Peter LAMOTTE, Wim |
Issue Date: | 2018 | Source: | IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019(1), p.1-24 | Abstract: | Sensitive cryptographic information, e.g. AES secret keys, can be extracted from the electromagnetic (EM) leakages unintentionally emitted by a device using techniques such as Correlation Electromagnetic Analysis (CEMA). In this paper, we introduce Correlation Optimization (CO), a novel approach that improves CEMA attacks by formulating the selection of useful EM leakage samples in a trace as a machine learning optimization problem. To this end, we propose the correlation loss function, which aims to maximize the Pearson correlation between a set of EM traces and the true AES key during training. We show that CO works with high-dimensional and noisy traces, regardless of time-domain trace alignment and without requiring prior knowledge of the power consumption characteristics of the cryptographic hardware. We evaluate our approach using the ASCAD benchmark dataset and a custom dataset of EM leakages from an Arduino Duemilanove, captured with a USRP B200 SDR. Our results indicate that the masked AES implementation used in all three ASCAD datasets can be broken with a shallow Multilayer Perceptron model, whilst requiring only 1,000 test traces on average. A similar methodology was employed to break the unprotected AES implementation from our custom dataset, using 22,000 unaligned and unfiltered test traces. | Keywords: | Correlation Optimization; Software Defined Radio; Correlation Electro-magnetic Analysis; correlation loss; machine learning | Document URI: | http://hdl.handle.net/1942/27465 | ISSN: | 2569-2925 | DOI: | 10.13154/tches.v2019.i1.1-24 | Rights: | Licensed under Creative Commons License CC-BY 4.0. | Category: | A1 | Type: | Journal Contribution | Validations: | vabb 2020 |
Appears in Collections: | Research publications |
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robyns2018improving.pdf | Published version | 2.53 MB | Adobe PDF | View/Open |
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