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 applied to have been introduced, and while the area of side-channel attacks and their mitigation is very well-researched, it is yet to be consolidated. Deep learning-based side-channel attacks entered the field in recent years with the promise of more competitive performance and enlarged attackers’ capabilities compared to other techniques. At the same time, the new attacks bring new challenges and complexities to the domain, making the systematization of knowledge (SoK) even more critical. We first dissect deep learning-based side-channel attacks according to the different phases they can be used in and map those phases to the efforts conducted so far in the domain. For each phase, we identify the weaknesses and challenges that triggered the known open problems. We also connect the attacks to the threat models and evaluate their advantages and drawbacks. Finally, we provide a number of recommendations to be followed in deep learning-based side-channel attacks.

The use of deep learning-based side-channel analysis is an effective way of performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research papers have reported successful results, they typically focus on profiling and attacking a single device, assuming that leakages are similar between devices of the same type. However, this assumption is not always realistic due to variations in hardware and measurement setups, creating what is known as the portability problem. Profiling multiple devices has been proposed as a solution, but obtaining access to these devices may pose a challenge for attackers. This paper proposes a new approach to overcome the portability problem by introducing a neural network layer assessment methodology based on the ablation paradigm. This methodology evaluates the sensitivity and resilience of each layer, providing valuable knowledge to create a Multiple Device Model from Single Device (MDMSD). Specifically, it involves ablating a specific neural network section and performing recovery training. As a result, the profiling model, trained initially on a single device, can be generalized to leakage traces measured from various devices. By addressing the portability problem through a single device, practical side-channel attacks could be more accessible and effective for attackers.

The deep learning-based side-channel analysis gave some of the most prominent side-channel attacks against protected targets in the past few years. To this end, the research community’s focus has been on creating the following: (1) powerful multilayer perceptron or convolutional neural network architectures and (2) (if possible) minimal multilayer perceptron or convolutional neural network architectures. Currently, we see that, computationally intensive hyperparameter tuning methods (e.g., Bayesian optimization or reinforcement learning) provide the best results. However, as targets with more complex countermeasures become available, these minimal architectures may be insufficient, and we will require novel deep learning approaches.This work explores how residual neural networks (ResNets) perform in side-channel analysis and how to construct deeper ResNets capable of working with larger input sizes and requiring minimal tuning. The resulting architectures, obtained by following our guidelines, are significantly deeper than commonly seen in side-channel analysis, require minimal hyperparameter tuning for specific datasets, and offer competitive performance with state-of-the-art methods across several datasets. Additionally, the results indicate that ResNets work especially well when the number of profiling traces and features in a trace is large.