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Entropy
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On Emerging Cryptographic Techniques
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On Emerging Cryptographic Techniques

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 2438

Special Issue Editor

Dr. Naveed Ahmed Azam

E-Mail Website
Guest Editor
Department of Applied Mathematics and Physics, Kyoto University, Kyoto 606-850, Japan
Interests: discrete mathematics; operations research; artificial intelligence; cryptography; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The demand for data security against modern cryptanalysis has increased significantly due to the progress in the fields of digital data communication and computation techniques. Entropy is an essential security parameter to quantify the randomness generation capability of a cryptographic algorithm. The aim of this issue is to encourage the development of new novel cryptographic algorithms that can guarantee, both theoretically and experimentally, optimal entropy and hence high-security resistance against modern computational attacks in real time. All emerging cryptographic techniques such as image encryption techniques and text encryption techniques are within the scope of this Special Issue.

Dr. Naveed Ahmed Azam
Guest Editor

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. Entropy is an international peer-reviewed open access monthly 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

  • elliptic curve cryptography
  • chaos-based cryptography
  • biological cryptography
  • hashing techniques
  • random number generators
  • substitution box generators
  • entropy analysis
  • nonlinearity analysis

Published Papers (1 paper)

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Research

14 pages, 1252 KiB  
Article
Deep-Learning-Based Cryptanalysis of Lightweight Block Ciphers Revisited
by Hyunji Kim, Sejin Lim, Yeajun Kang, Wonwoong Kim, Dukyoung Kim, Seyoung Yoon and Hwajeong Seo
Entropy 2023, 25(7), 986; https://doi.org/10.3390/e25070986 - 28 Jun 2023
Cited by 3 | Viewed by 1839
Abstract
With the development of artificial intelligence, deep-learning-based cryptanalysis has been actively studied. There are many cryptanalysis techniques. Among them, cryptanalysis was performed to recover the secret key used for cryptography encryption using known plaintext. In this paper, we propose a cryptanalysis method based [...] Read more.
With the development of artificial intelligence, deep-learning-based cryptanalysis has been actively studied. There are many cryptanalysis techniques. Among them, cryptanalysis was performed to recover the secret key used for cryptography encryption using known plaintext. In this paper, we propose a cryptanalysis method based on state-of-art deep learning technologies (e.g., residual connections and gated linear units) for lightweight block ciphers (e.g., S-DES, S-AES, and S-SPECK). The number of parameters required for training is significantly reduced by 93.16%, and the average of bit accuracy probability increased by about 5.3% compared with previous the-state-of-art work. In addition, cryptanalysis for S-AES and S-SPECK was possible with up to 12-bit and 6-bit keys, respectively. Through this experiment, we confirmed that the-state-of-art deep-learning-based key recovery techniques for modern cryptography algorithms with the full round and the full key are practically infeasible. Full article
(This article belongs to the Special Issue On Emerging Cryptographic Techniques)
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