Applications of Artificial Intelligence to Cryptography

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 1 January 2025 | Viewed by 2772

Special Issue Editors


E-Mail Website
Guest Editor
Services and Cybersecurity Group, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Interests: symmetric cryptography; evolutionary computation; cellular automata; boolean functions; combinatorial designs
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milan-Bicocca, Milano, MI, Italy
Interests: natural computing; unconventional computing; membrane computing; theoretical computer science

Special Issue Information

Dear Colleagues,

For about four decades, artificial intelligence (AI) has provided an interesting set of techniques for the design and analysis of cryptographic primitives and protocols. Indeed, this fruitful interaction has been witnessed by multiple publications which have targeted, for instance, the use of AI-based metaheuristics to solve optimization problems related to the design of cryptographic primitives, as well as the adoption of machine learning classification models to analyze the security of such primitives. This research thread is well-established in the literature, while still featuring plenty of new directions and open problems to be addressed.

The aim of this Special Issue is to gather the most recent results and works where AI plays a significant role in the design or the analysis of cryptographic primitives and protocols. Relevant topics include (but are not limited to): the use of bio-inspired optimization approaches (such as evolutionary algorithms and swarm intelligence) to construct symmetric primitives such as Boolean functions and S-boxes; the design of primitives and protocols using AI-based computational models, such as cellular automata and neural networks; deep learning models for cryptoanalysis and side-channel attacks. In addition to novel research contributions, we also welcome surveys and systematization of knowledge papers covering applications of AI methods to cryptography.

Dr. Luca Mariot
Dr. Alberto Leporati
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • cryptography
  • cryptanalysis
  • symmetric ciphers
  • pseudorandom number generators
  • side-channel analysis
  • neural networks
  • deep learning
  • evolutionary algorithms
  • cellular automata

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 826 KiB  
Article
Resolving the Doubts: On the Construction and Use of ResNets for Side-Channel Analysis
by Sengim Karayalcin, Guilherme Perin and Stjepan Picek
Mathematics 2023, 11(15), 3265; https://doi.org/10.3390/math11153265 - 25 Jul 2023
Viewed by 1357
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence to Cryptography)
Show Figures

Figure 1

20 pages, 572 KiB  
Article
NASCTY: Neuroevolution to Attack Side-Channel Leakages Yielding Convolutional Neural Networks
by Fiske Schijlen, Lichao Wu and Luca Mariot
Mathematics 2023, 11(12), 2616; https://doi.org/10.3390/math11122616 - 7 Jun 2023
Cited by 2 | Viewed by 806
Abstract
Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be [...] Read more.
Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be utilized to perform highly effective SCA profiling, even against countermeasure-hardened targets. This study investigates a new approach to designing NNs for SCA, called neuroevolution to attack side-channel traces yielding convolutional neural networks (NASCTY-CNNs). This method is based on a genetic algorithm (GA) that evolves the architectural hyperparameters to automatically create CNNs for side-channel analysis. The findings of this research demonstrate that we can achieve performance results comparable to state-of-the-art methods when dealing with desynchronized leakages protected by masking techniques. This indicates that employing similar neuroevolutionary techniques could serve as a promising avenue for further exploration. Moreover, the similarities observed among the constructed neural networks shed light on how NASCTY effectively constructs architectures and addresses the implemented countermeasures. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence to Cryptography)
Show Figures

Figure 1

Back to TopTop