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Claim

Towards Efficient Training in Deep Learning Side-Channel Attacks

Research datakeyboard_double_arrow_right Dataset 11 Apr 2023 English Publisher:Zenodo
Authors: Castellazzi, Luca;

doi: 10.5281/zenodo.7817187 , 10.5281/zenodo.7835461 , 10.5281/zenodo.7817188 , 10.5281/zenodo.7817256 , 10.5281/zenodo.7833667

Towards Efficient Training in Deep Learning Side-Channel Attacks

Abstract

Datasets used to develop my Master's Thesis Towards Efficient Training in Deep Learning Side-Channel Attacks at Politecnico di Milano. The datasets contain power consumption measurements taken from multiple Riscure Piñata (STM32F4) boards (3) considering multiple keys (11) while executing AES-128. unprotected-AES.zip contains the traces related to the execution of a software unprotected implementation of AES-128. masked-AES.zip contains the traces related to the execution of a software masked implementation of AES-128.

Related Organizations
Keywords

Cryptography, Cyberattack, Deep Learning, Side-Channel Attacks

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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8