This article, for the first time, demonstrates Cross-device Deep Learning Side-Channel
Attack (X-DeepSCA), achieving an accuracy of> 99.9%, even in presence of significantly …

We present the first deep learning-based side-channel attack on AES-128 using far field
electromagnetic emissions as a side channel. Our neural networks are trained on traces …

Side-channel attacks have become a realistic threat to implementations of cryptographic
algorithms, especially with the help of deep-learning techniques. The majority of recently …

Deep learning side-channel attacks are an emerging threat to the security of
implementations of cryptographic algorithms. The attacker first trains a model on a large set …

Recently, several deep-learning side-channel attacks on cryptographic algorithms were
demonstrated. With the help of a trained deep-learning model, the attacker extracts the key …

Several attacks on AES using far field electromagnetic (EM) emission as a side channel
have been recently presented. Unlike power analysis or near filed EM analysis, far field EM …

Recently introduced federated learning is an attractive framework for the distributed training
of deep learning models with thousands of participants. However, it can potentially be used …

In this paper we show that deep learning can be used to identify the shape of power traces
corresponding to the responses of a protected arbiter PUF implemented in FPGAs. To …

With the growth of popularity of Field-Programmable Gate Arrays (FPGAs) in cloud
environments, new paradigms such as FPGA-as-a-Service (FaaS) emerge. This challenges …

There are different ways to leverage Side Channel information into a successful attack
against cryptographic hardware. The most constraint attack scenario assumes no …