The Internet of Things (IoT) has continued gaining in popularity and importance in everyday
life in recent years. However, this development does not only present advantages. Indeed …

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 …

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

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 …

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 …

Today, the deep learning-based side-channel analysis represents a widely researched
topic, with numerous results indicating the advantages of such an approach. Indeed …

The adoption of deep neural networks for profiled side-channel attacks provides powerful
options for leakage detection and key retrieval of secure products. When training a neural …

In this work, we ask a question whether Convolutional Neural Networks are more suitable for
side-channel attacks than some other machine learning techniques and if yes, in what …

In the profiled side-channel analysis, deep learning-based techniques proved to be very
successful even when attacking targets protected with countermeasures. Still, there is no …