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Symmetry
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Frontiers in Cryptography
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Frontiers in Cryptography

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7026

Special Issue Editors

Dr. Ding Wang

E-Mail Website
Guest Editor
College of CyberScience, Nankai University, Tianjin 300071, China
Interests: password; user authentication; cryptanalysis; provable security
Dr. Weizhi Meng

E-Mail Website
Guest Editor
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Interests: cyber security; intrusion detection; mobile security and authentication; HCI security; malware analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Shen

E-Mail Website
Guest Editor
Graduate School of Nanjing University of Information Science & Technology, Jiangsu, Nanjing 210044, China
Interests: information security; public crypto

Special Issue Information

Dear Colleagues, 

Cryptanalysis is generally thought of as exploring the weaknesses of the underlying mathematics of a cryptographic system, but it also includes looking for weaknesses in implementation, such as side-channel attacks or weak entropy inputs against symmetric or asymmetric cryptosystems. It is an important component of the process of creating strong cryptosystems. With the development of new technologies and the emergence of new security threats in cryptosystems, many new methods are being proposed to analyze the security of the cryptographic algorithm or cryptographic system, for example, the machine-learning-based cryptanalysis methods. Many interesting new issues are also emerging, such as the vulnerabilities in the schemes selected by the NIST for the finalist round.

The goal of this Special Issue is to foster the dissemination of the latest technologies, solutions,  results, and prototypes regarding cryptanalysis. We are soliciting contributions (research articles) covering a broad range of topics on cryptanalysis, including, but not limited to, the following:

  1. Machine Learning-Based Cryptanalysis;
  2. Side-channel attacks and countermeasures;
  3. Fault attacks and countermeasures;
  4. Hardware tampering and tamper-resistance;
  5. White-box cryptography and code obfuscation;
  6. Hardware and software reverse engineering;
  7. Verification methods and tools for secure design;
  8. Special-purpose hardware for cryptanalysis;
  9. Leakage-resilient cryptography;
  10. Cryptanalysis and evaluation tools.

Dr. Ding Wang
Dr. Weizhi Meng
Prof. Dr. Jian Shen
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. Symmetry 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 2400 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.

Published Papers (4 papers)

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Research

20 pages, 435 KiB  
Article
Application of Discrete Pruned Enumeration in Solving BDD
by Luan Luan, Yanan Shi, Chunxiang Gu and Yonghui Zheng
Symmetry 2023, 15(2), 355; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15020355 - 28 Jan 2023
Viewed by 874
Abstract
The bounded distance decoding (BDD) is a fundamental problem in lattice-based cryptography which is derived from the closest vector problem (CVP). In this paper, we adapt the lattice enumeration with discrete pruning, a burgeoning method for the shortest lattice vector problem (SVP), to [...] Read more.
The bounded distance decoding (BDD) is a fundamental problem in lattice-based cryptography which is derived from the closest vector problem (CVP). In this paper, we adapt the lattice enumeration with discrete pruning, a burgeoning method for the shortest lattice vector problem (SVP), to solve BDD in various cryptanalysis scenarios using direct method. We first transfer the basic definition involved in discrete pruning technique from SVP to CVP, prove corresponding properties and give the specific procedures of the algorithm. Additionally, we use the discrete pruning technique to interpret the classical CVP algorithms, including Babai’s nearest plane and Lindner–Peikert nearest planes, which can be regarded as discrete pruned enumeration on some special pruning sets. We propose three probability models in the runtime analysis to accurately estimate the cost of our algorithm in different application scenarios. We study the application of discrete pruned enumeration for BDD mainly on LWE-based cryptosystem and DSA with partially known nonces. The experimental results show that our new algorithm has higher efficiency than the previous algorithms which directly solve BDD, including the nearest plane(s) algorithms and the lattice enumeration with classical pruning strategies, and we are able to recover the DSA secret with less leaked information than the previous works. Full article
(This article belongs to the Special Issue Frontiers in Cryptography)
21 pages, 6565 KiB  
Article
Research on a Vehicle Authentication and Key Transmission Protocol Based on CPN
by Lu Zheng and Tao Feng
Symmetry 2022, 14(11), 2398; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14112398 - 13 Nov 2022
Cited by 1 | Viewed by 1545
Abstract
With the rapid development of the Internet of Vehicles, the increase in vehicle functional requirements has led to the continuous increase in complex electronic systems, and the in-vehicle network is extremely vulnerable to network attacks. The controller area network (CAN) bus is the [...] Read more.
With the rapid development of the Internet of Vehicles, the increase in vehicle functional requirements has led to the continuous increase in complex electronic systems, and the in-vehicle network is extremely vulnerable to network attacks. The controller area network (CAN) bus is the most representative in-vehicle bus technology in intra-vehicular networks (IVNs) for its flexibility. Although the current framework to protect the safety of CAN has been proposed, the safety communication mechanism between electronic control units (ECUs) in the vehicle network is still weak. A large number of communication protocols focus on the addition of safety mechanisms, and there is a lack of general protocol formal modeling and security assessment. In addition, many protocols are designed without considering key updates and transmission, ECUs maintenance, etc. In this work, we propose an efficient in-vehicle authentication and key transmission scheme. This scheme is a certificateless framework based on identity cryptography, which can not only ensure the security of the in-vehicle network but also meet the real-time requirements between ECUs. Moreover, this scheme can reduce the complexity of key management for centralized key generators. To evaluate the security of this scheme, we adopt a protocol model detection method based on the combination of the colored Petri net (CPN) and the Dolev–Yao attack model to formally evaluate the proposed protocol. The evaluation results show that the proposed scheme can effectively prevent three types of man-in-the-middle attacks. Full article
(This article belongs to the Special Issue Frontiers in Cryptography)
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9 pages, 416 KiB  
Article
(Quantum) Time-Memory-Data Tradeoff Attacks on the SNOW-V Stream Cipher
by Sijia Li, Zhiyi Liao, Zhengyang Wu, Zheng Wu and Lin Ding
Symmetry 2022, 14(6), 1127; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14061127 - 30 May 2022
Cited by 1 | Viewed by 1611
Abstract
Symmetric cryptosystems (i.e., stream ciphers and block ciphers) have always played an important part in securing the various generations of 3GPP (3rd Generation Partnership Project) mobile telephony systems. The SNOW-V stream cipher, published in September 2019, is the most recent member of the [...] Read more.
Symmetric cryptosystems (i.e., stream ciphers and block ciphers) have always played an important part in securing the various generations of 3GPP (3rd Generation Partnership Project) mobile telephony systems. The SNOW-V stream cipher, published in September 2019, is the most recent member of the well-known SNOW family of ciphers. It is designed to provide confidentiality and integrity for 5G communications. There have been no time-memory-data tradeoff (TMDTO) attacks on the cipher published so far. By combining with the BSW sampling technique, we propose TMDTO attacks on SNOW-V. The results show that the attacker can mount a TMDTO attack, where none of the online time complexity, the memory complexity and the offline time complexity are bigger than 2256, if the keystream sequences generated by the secret key, together with different IVs, are provided to the attacker. Furthermore, we analyze the security of SNOW-V against quantum TMDTO attacks, and the results show that a quantum TMDTO attack offers, strictly, better online time complexity than Grover’s algorithm, when the available memory space is bigger than 2170.67. These results are helpful in evaluating the security of SNOW-V against (quantum) TMDTO attacks. Full article
(This article belongs to the Special Issue Frontiers in Cryptography)
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14 pages, 1840 KiB  
Article
An Efficient Deep Unsupervised Domain Adaptation for Unknown Malware Detection
by Fangwei Wang, Guofang Chai, Qingru Li and Changguang Wang
Symmetry 2022, 14(2), 296; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14020296 - 01 Feb 2022
Cited by 6 | Viewed by 1949
Abstract
As an innovative way of communicating information, the Internet has become an indispensable part of our lives. However, it also facilitates a more widespread attack of malware. With the assistance of modern cryptanalysis, emerging malware having symmetric properties, such as encryption and decryption, [...] Read more.
As an innovative way of communicating information, the Internet has become an indispensable part of our lives. However, it also facilitates a more widespread attack of malware. With the assistance of modern cryptanalysis, emerging malware having symmetric properties, such as encryption and decryption, pack and unpack, presents new challenges to effective malware detection. Currently, numerous malware detection approaches are based on supervised learning. The biggest challenge is that the existing systems rely on a large amount of labeled data, which is usually difficult to gain. Moreover, since the newly emerging malware has a different data distribution from the original training samples, the detection performance of these systems will degrade along with the emergence of new malware. To solve these problems, we propose an Unsupervised Domain Adaptation (UDA)-based malware detection method by jointly aligning the distribution of known and unknown malware. Firstly, the distribution divergence between the source and target domain is minimized with the help of symmetric adversarial learning to learn shared feature representations. Secondly, to further obtain semantic information of unlabeled target domain data, this paper reduces the class-level distribution divergence by aligning the class center of labeled source and pseudo-labeled target domain data. Finally, we mainly use a residual network with a self-attention mechanism to extract more accurate feature information. A series of experiments are performed on two public datasets. Experimental results illustrate that the proposed approach outperforms the existing detection methods with an accuracy of 95.63% and 95.04% in detecting unknown malware on two datasets, respectively. Full article
(This article belongs to the Special Issue Frontiers in Cryptography)
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