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.

Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li, Stjepan Picek. Findings of the Association for Computational Linguistics ACL 2024. 2024.

The prediction and design of photonic features have traditionally been guided by theory‐driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data‐driven methods for photonics. Here, we demonstrate several predictive and generative data‐driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two‐dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high‐accuracy convolutional neural network for band structure prediction, with orders‐of‐magnitude speedup compared to conventional theory‐driven solvers. Separately, we demonstrate an approach to high‐throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse‐magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data‐driven tools in photonics and the natural sciences.

Substitution Boxes (S-Boxes) play an important role in many modern-day cryptographic algorithms, more commonly known as ciphers. Without carefully chosen S-Boxes, such ciphers would be easier to break. Therefore, it is not surprising that the design of suitable S-Boxes attracts a lot of attention in the cryptography community. The evolutionary computation (EC) community also had several attempts using evolutionary paradigms to evolve S-Boxes with good cryptographic properties. This article focuses on a fitness function one should use when evolving highly nonlinear S-Boxes. After an extensive experimental analysis of the current state-of-the-art fitness functions, we present a new one that offers higher speed and better results when compared with the aforementioned fitness functions.

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.