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 …

A major goal of neuroimaging studies is to develop predictive models to analyze the
relationship between whole brain functional connectivity patterns and behavioural traits …

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

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

This paper presents a side-channel analysis (SCA) on key encapsulation mechanism (KEM)
based on the Fujisaki–Okamoto (FO) transformation and its variants. The FO transformation …

Side Channel Analysis (SCA) presents a clear threat to privacy and security in modern
computing systems. The vast majority of communications are secured through cryptographic …

Recently, several studies have been published on the application of deep learning to
enhance Side-Channel Attacks (SCA). These seminal works have practically validated the …

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

One of the main promoted advantages of deep learning in profiling sidechannel analysis is
the possibility of skip** the feature engineering process. Despite that, most recent …

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 …