Side-channel attacks represent a realistic and serious threat to the security of embedded
devices for already almost three decades. A variety of attacks and targets they can be …

Hardware security and trust have become a pressing issue during the last two decades due
to the globalization of the semiconductor supply chain and ubiquitous network connection of …

Recent works have demonstrated that deep learning algorithms were efficient to conduct
security evaluations of embedded systems and had many advantages compared to the other …

Template attack is the most common and powerful profiled side channel attack. It relies on a
realistic assumption regarding the noise of the device under attack: the probability density …

Profiled side-channel analysis based on deep learning, and more precisely Convolutional
Neural Networks, is a paradigm showing significant potential. The results, although scarce …

In the last decade, there has been growing interest in Vehicular Ad Hoc NETworks
(VANETs). Today car manufacturers have already started to equip vehicles with …

Deep learning represents a powerful set of techniques for profiling side-channel analysis.
The results in the last few years show that neural network architectures like multilayer …

We concentrate on machine learning techniques used for profiled sidechannel analysis in
the presence of imbalanced data. Such scenarios are realistic and often occurring, for …

The reverse engineering (RE) of electronic chips and systems can be used with honest and
dishonest intentions. To inhibit RE for those with dishonest intentions (eg, piracy and …

The side-channel community recently investigated a new approach, based on deep
learning, to significantly improve profiled attacks against embedded systems. Previous …