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 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 …

With increasing expansion of the Internet of Things, embedded devices equipped with
cryptographic modules become an important factor to protect sensitive data. Even though …

Electronic systems rely on efficient hardware, popularly known as system-on-chip (SoC), to
support its core functionalities. A typical SoC consists of diverse components gathered from …

Profiled side-channel attacks represent a practical threat to digital devices, thereby having
the potential to disrupt the foundation of e-commerce, the Internet of Things (IoT), and smart …

Existing power analysis techniques rely on strong adversary models with prior knowledge of
the leakage or training data. We introduce side-channel analysis with unsupervised learning …

Side-channel attacks represent one of the most powerful categories of attacks on
cryptographic devices with profiled attacks in a prominent place as the most powerful among …

We consider the question whether synchronization/alignment methods are still
useful/necessary in the context of side-channel attacks exploiting deep learning algorithms …

Profiled side-channel attacks represent the most powerful category of side-channel attacks.
There, the attacker has access to a clone device to profile its leaking behavior. Additionally …

The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the
objective of which was to improve and adapt existent cryptanalysis methodologies and tools …