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 …

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 …

Side-channel attacks exploit a physical observable originating from a cryptographic device
in order to extract its secrets. Many practically relevant advances in the field of side-channel …

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 …

This study proposes a multi-objective optimization (MOO) framework for optimizing concrete
mixture proportions. Advanced methods such as K-fold cross-validation, Bayesian …

The efforts to decrease air pollutants using renewable energies, especially photovoltaic
energy, are develo** rapidly worldwide. Photovoltaic powerhouses contain a large …

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

In recent years, the threat of profiling attacks using deep learning has emerged. Successful
attacks have been demonstrated against various types of cryptographic modules. However …

This study derives information-theoretical bounds of the success rate (SR) of side-channel
attacks on masked implementations. We first develop a communication channel model …

Deep learning is a powerful direction for profiling side-channel analysis as it can break
targets protected with countermeasures even with a relatively small number of attack traces …