Description
The success of horizontal side-channel attacks heavily depends on the quality of the traces as well as the correct extraction of interest areas, which are expected to contain relevant leakages. If former is insufficient, this will consequently degrade the identification capability of potential leakage candidates and often render attacks inapplicable. This work assess the relevance of neural networks in the unsupervised context of horizontal attacks to mitigate noise artefacts from the input signal by proposing two methods with alternative training objectives. Their application results in enhanced traces quality and better exploitability using clustering-based horizontal attacks.
Infos pratiques
Prochains exposés
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Securing processor's microarchitecture against SCA in a post-quantum cryptography setting
Orateur : Vincent MIGLIORE - LAAS-CNRS
Hardware microarchitecture is a well-known source of side-channel leakages, providing a notable security reduction of standard cryptographic algorithms (e.g. AES) if not properly addressed by software or hardware. In this talk, we present new design approaches to harden processor's microarchitecture against power-based side-channel attacks, relying on configurable and cascadable building blocks[…]-
SemSecuElec
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Side-channel
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Micro-architectural vulnerabilities
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Onysis: A secure European SoC FPGA
Orateur : Adrien GRASSEIN - Nanoxplore
Developed in collaboration with the DGA, the Onysis project introduces a European SoC FPGA designed to embed advanced hardware security features. This presentation will provide an overview of the Onysis architecture, focusing specifically on its native mechanisms to protect critical systems. We will detail the implementation of its integrated security subsystem, covering the secure boot sequence[…]-
SemSecuElec
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Using High Level Profiling Data to Early Assess the Fault Tolerance of Complex Digital Components
Orateur : Luc NOIZETTE - Nuclétudes (filiale Ariane group)
This presentation outlines an innovative methodology for estimating the fault tolerance of complex components based on application profiling obtained using a high-level virtual platform. A derating factor, derived exclusively from profiling metrics (e.g., lifetime in memory and registers), is calibrated using a reliability dataset collected from a set of benchmarks. Applying it to test softwares[…]-
SemSecuElec
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Fault injection
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