Léo Weissbart - PhD Student at TU Delft and Radboud University
Léo Weissbart [--cv] [--gh] [--mail] [-l]
Léo Weissbart is a PhD student at EEMCS INSY of TU Delft and the Digital Security group of the Radboud University,
working in the area of physical attacks and machine learning under the
supervision of Stepjan Picek and Lejla Batina.
Before that, he was an engineering student at the
Grenoble INP-Esisar (National Institute of advanced systems and networks, France).
λ x y.
x @ y.nl
Mercator 1, 03.11
6525 EC Nijmegen
Hardware Security, Deep Learning, Implementation of Cryptography, Physical Attacks and Countermeasures
PUB-2018 -Side-Channel Attack using Order 4 Element against Curve25519 on ATMega328P [PDF]
Yoshinori Uetake, Akihiro Sanada, Takuya Kusaka, Yasuyuki Nogami, Léo Weissbart and Sylvain Duquesne
The International Symposium on Information Theory and Its Applications - ISITA 2018
With the matter of secure communication between devices, and especially for IoT devices, more and more applications need trustful protocols to communicate using public key cryptography. Elliptic curve cryptography is nowadays a very secure and efficient public key cryptography method. One of the most recent and secure curve is Curve25519 and one of its failure is attack on low-order elements during a Diffie-Hellman key exchange. This document demonstrates that an attack using an order 4 point is possible on an embedded system with a simple power analysis, pointing out every IoT using Curve255119 as a cryptographic method, a potential target to side-channel attacks.
PUB-2019-358 -One trace is all it takes: Machine Learning-based Side-channel Attack on EdDSA [PDF]
Léo Weissbart, Stjepan Picek and Lejla BatinaProfiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same time, the results for implementations public-key cryptosystems are very sparse. In this paper, we consider several machine learning techniques in order to mount a power analysis attack on EdDSA using the curve Curve25519 as implemented in WolfSSL. The results show all considered techniques to be viable and powerful options. The results with convolutional neural networks (CNNs) are especially impressive as we are able to break the implementation with only a single measurement in the attack phase while requiring less than 500 measurements in the training phase. Interestingly, that same convolutional neural network was recently shown to perform extremely well for attacking the AES cipher. Our results show that some common grounds can be established when using deep learning for profiling attacks on distinct cryptographic algorithms and their corresponding implementations.
2019 - June. Summer School on real-world crypto and privacy - Šibenik
Summer school jointly organized by the Digital Security (DiS) group, Radboud University (The Netherlands), ETH Zurich Information Security and Privacy Center (Switzerland) and Faculty of Electrical Engineering and Computing, University of Zagreb (Croatia).