Non profiled side-channel attacks aim at exploiting leakage traces from a targeted embedded system to extract secret information, without a priori knowledge on the true leakage model of the device. To automate and simplify attacks, deep learning techniques have thus been introduced in the side-channel community. Most of the works published have mainly explored the use of discriminative models in the profiled or non profiled context. However, the lack of interpretability and explainability of...

The benefits of using Deep Learning techniques to enhance side-channel attacks performances have been demonstrated over recent years. Most of the work carried out since then focuses on discriminative models. However, one of their major limitations is the lack of theoretical results. Indeed, this lack of theoretical results, especially concerning the choice of neural network architecture to consider or the loss to prioritize to build an optimal model, can be problematic for both attackers...

Cryptojacking, the unauthorised use of computing resources to mine cryptocurrency, has emerged as a critical threat in today’s digital landscape. These attacks not only compromise system integrity but also result in increased costs, reduced hardware lifespan, and heightened network security risks. Early and accurate detection is essential to mitigate the adverse effects of cryptojacking. This study focuses on developing a semi-supervised machine learning (ML) approach that leverages an...

Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different underlying leakage distributions. Consequently, the same profiling model hyperparameters are usually not equally efficient for other targets. This paper...

Side-channel attacks are powerful non-invasive attacks on cryptographic algorithms. Among such attacks, profiling attacks have a prominent place as they assume an attacker with access to a copy of the device under attack. The attacker uses the device's copy to learn as much as possible about the device and then mount the attack on the target device. In the last few years, Machine Learning has been successfully used in profiling attacks, as such techniques proved to be capable of breaking...

Over the recent years, the cryptanalysis community leveraged the potential of research on Deep Learning to enhance attacks. In particular, several studies have recently highlighted the benefits of Deep Learning based Side-Channel Attacks (DLSCA) to target real-world cryptographic implementations. While this new research area on applied cryptography provides impressive result to recover a secret key even when countermeasures are implemented (e.g. desynchronization, masking schemes), the lack...

Deep learning (DL)-based profiled attack has been proved to be a powerful tool in side-channel analysis. A variety of multi-layer perception (MLP) networks and convolutional neural networks (CNN) are thereby applied to cryptographic algorithm implementations for exploiting correct keys with a smaller number of traces and a shorter time. However, most attacks merely focus on small datasets, in which their points of interest are well-trimmed for attacks. Countermeasures applied in embedded...

This paper introduces a practical evaluation procedure based on autoencoders for profiled side-channel analysis evaluations. An autoencoder is a learning model able to pre-process leakage traces improving in this way the guessing entropy. Nevertheless, this learning model's design should aim to code the leakage distribution to avoid relevant information being removed. For this reason, we propose an autoencoder built upon dilated convolutions. When using these learning models, the evaluation...

In recent years, deep learning-based side-channel attacks have established their position as mainstream. However, most deep learning techniques for cryptanalysis mainly focused on classifying side-channel information in a profiled scenario where attackers can obtain a label of training data. In this paper, we introduce a novel approach with deep learning for improving side-channel attacks, especially in a non-profiling scenario. We also propose a new principle of training that trains an...

In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that deep learning attacks will always succeed. Various countermeasures make attacks significantly more complicated, and those countermeasures can be further combined to make the attacks even more challenging. An intuitive solution to improve the performance of attacks would be to reduce the effect of...