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Feature Extraction for Side-Channel Attacks

Abstract : Cryptographic integrated circuits may be vulnerable to attacks based on the observation of information leakages conducted during the cryptographic algorithms' executions, the so-called Side-Channel Attacks. Nowadays the presence of several countermeasures may lead to the acquisition of signals which are at the same time highly noisy, forcing an attacker or a security evaluator to exploit statistical models, and highly multi-dimensional, letting hard the estimation of such models. In this thesis we study preprocessing techniques aiming at reducing the dimension of the measured data, and the more general issue of information extraction from highly multi-dimensional signals. The first works concern the application of classical linear feature extractors, such as Principal Component Analysis and Linear Discriminant Analysis. Then we analyse a non-linear generalisation of the latter extractor, obtained through the application of a « Kernel Trick », in order to let such preprocessing effective in presence of masking countermeasures. Finally, further generalising the extraction models, we explore the deep learning methodology, in order to reduce signal preprocessing and automatically extract sensitive information from rough signal. In particular, the application of the Convolutional Neural Network allows us to perform some attacks that remain effective in presence of signal desynchronisation.
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Submitted on : Friday, February 28, 2020 - 3:26:11 PM
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  • HAL Id : tel-02494260, version 1


Eleonora Cagli. Feature Extraction for Side-Channel Attacks. Cryptography and Security [cs.CR]. Sorbonne Université, 2018. English. ⟨NNT : 2018SORUS295⟩. ⟨tel-02494260⟩



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