To ensure that no information unintentionally leaks through side channels during the execution of cryptographic operations, the physical security of a device must be evaluated. Nowadays, a security analysis must show security not only against traditional Side-Channel Analysis (SCA) attacks (e.g., Differential Power Analysis (DPA)) involving classical statistical analysis but also against machine learning and deep learning attacks. If not protected against these attacks, symmetric and public-key cryptographic implementations can be at risk.

While traditional SCA attacks rely on a cryptanalyst’s expertise to extract features from the leakages of one or multiple traces and analyze their observations through statistical methods to recover the secret key. Deep Learning-based Side- Channel Analysis (DLSCA) attacks bring a new perspective to the field. DLSCA attacks rely on automating feature extraction using a task-specific algorithm. For most DLSCA attacks, an expert is still needed, but the expert’s work is shifted to training this algorithm. Among the different deep learning architectures, the most used in DLSCA are the Multilayer Perceptron (MLP) and the Convolutional Neural Networks (CNN). Those methods are Neural Networks (NN) trained to find patterns in a collected dataset of side-channel traces to recover the secret key given a proper tuning of their hyperparameters and a successful training process.

This thesis investigates the use of deep learning in side-channel analysis of symmetric and public-key cryptography and other applications of side-channel analysis. We go through the application of DLSCA for implementations of AES and ASCON in symmetric cryptography and EdDSA in public-key cryptography. We also explore the use of deep learning to enhance TEMPEST-like side-channel analysis and the use of side-channel analysis to reverse engineer neural networks.

The main contributions of this thesis are as follows. First, we show the performances that can reach a MLP on a dataset of an AES implementation protected with a masking countermeasure. We demonstrate that MLP can defeat the masking countermeasure and recover the secret key with a high success rate for many configurations of hyperparameters and power intermediate models and even with very few parameters.

Second, we present an application of CNN in the side-channel analysis of the lightweight authenticated encryption algorithm ASCON on a 32-bit microcontroller. We demonstrate that the reference implementation is vulnerable to DLSCA attacks and that the same attack can be applied to a masked implementation but cannot completely recover the secret key.

Third, we propose a single-trace attack on the ephemeral key of EdDSA on the elliptic curve 25519. We show that the attack can recover the secret key from a single execution of an implementation on a 32-bit microcontroller. This attack is based on a CNN, and we demonstrate that, of the other profiling methods explored, the CNN is the most efficient for this attack. Furthermore, we systematize this attack and show that it can be applied to a different target and implement countermeasures against side-channel analysis.

Finally, we demonstrate the use of side-channel analysis and deep learning in different applications than cryptographic implementations. We present a methodology to evaluate TEMPEST attacks using deep learning. We focus the analysis of the electromagnetic emanations of mobile devices without visual line of sight, to build a testbed with a standard setup that can be used to test different attacker models. A second application is the use of side-channel analysis to reverse engineer neural networks on GPU. We show that side-channel analysis of the electromagnetic emanations of a GPU can be used to recover several hyperparameters of a neural network during the inference phase.

Our main research goal is to apply deep learning to side-channel analysis to develop new attacks for existing implementations and countermeasures, and we believe that this thesis is a step in that direction regarding the aforementioned contributions. We also believe that the reading of this thesis will shine the light on the potential of deep learning in side-channel analysis and inspire future research in this field to help to secure the electronics of tomorrow.