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Symmetry
Special Issues
Symmetry and Asymmetry in Cybersecurity
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Symmetry and Asymmetry in Cybersecurity

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2884

Special Issue Editor

Dr. Jiale Zhang

E-Mail Website
Guest Editor
School of Information Engineering, Yangzhou University, Yangzhou 225009, China
Interests: AI security and privacy protection; blockchain security; federated learning; moving edge computing

Special Issue Information

Dear Colleague,

Cybersecurity is a rapidly evolving field, and understanding the dynamics of symmetry and asymmetry is crucial in addressing emerging threats and vulnerabilities. Symmetry and asymmetry in cybersecurity refer to the balance or imbalance of power, resources, and strategies between attackers and defenders in the digital realm. This interplay has a profound impact on the effectiveness of security measures, the resilience of systems, and the overall state of cybersecurity.

This Special Issue aims to explore the concept of symmetry and asymmetry in the context of cybersecurity. We invite researchers, scholars, and practitioners to contribute original research articles, reviews, and case studies that delve into various aspects of this theme.

Topics of Interest are (but are not limited to):

  1. Symmetric and Asymmetric Cyber Warfare: Analyzing the strategies, tactics, and tools employed by nation-states and non-state actors in cyber conflicts;
  2. Attack–Defense Asymmetry: Investigating the disparity between cyber attackers and defenders and the strategies used to mitigate this imbalance;
  3. Economic Aspects of Cybersecurity: Exploring the cost-effectiveness of cybersecurity measures and the economics of cybercrime;
  4. Human Aspects of Cybersecurity: Examining the role of human factors, psychology, and behavior in symmetric and asymmetric cyber-attacks;
  5. Machine Learning and AI in Cybersecurity: Assessing the use of AI-driven technologies in both offensive and defensive cyber operations;
  6. Legal and Ethical Dimensions: Discussing the legal and ethical implications of cyber conflict, including state-sponsored cyber activities;
  7. Resilience and Recovery: Strategies and techniques for building resilient systems and effective recovery plans in the face of cyber threats;
  8. Cybersecurity Policy and Governance: Analyzing government policies and international agreements related to cyber warfare and defense.

Dr. Jiale Zhang
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. 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.

Keywords

  • network security
  • applied cryptography
  • symmetric/asymmetry
  • cryptography attack
  • detection and mitigation
  • AI security
  • software security
  • data security
  • privacy preservation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

14 pages, 2915 KiB  
Article
Missing Data Imputation Based on Causal Inference to Enhance Advanced Persistent Threat Attack Prediction
by Xiang Cheng, Miaomiao Kuang and Hongyu Yang
Symmetry 2024, 16(11), 1551; https://doi.org/10.3390/sym16111551 - 19 Nov 2024
Viewed by 350
Abstract
With the continuous development of network security situations, the types of attacks increase sharply, but can be divided into symmetric attacks and asymmetric attacks. Symmetric attacks such as phishing and DDoS attacks exploit fixed patterns, resulting in system crashes and data breaches that [...] Read more.
With the continuous development of network security situations, the types of attacks increase sharply, but can be divided into symmetric attacks and asymmetric attacks. Symmetric attacks such as phishing and DDoS attacks exploit fixed patterns, resulting in system crashes and data breaches that cause losses to businesses. Asymmetric attacks such as Advanced Persistent Threat (APT), a highly sophisticated and organized form of cyber attack, because of its concealment and complexity, realize data theft through long-term latency and pose a greater threat to organization security. In addition, there are challenges in the processing of missing data, especially in the application of symmetric and asymmetric data filling, the former is simple but not flexible, and the latter is complex and more suitable for highly complex attack scenarios. Since asymmetric attack research is particularly important, this paper proposes a method that combines causal discovery with graph autoencoder to solve missing data, classify potentially malicious nodes, and reveal causal relationships. The core is to use graphic autoencoders to learn the underlying causal structure of APT attacks, with a special focus on the complex causal relationships in asymmetric attacks. This causal knowledge is then applied to enhance the robustness of the model by compensating for data gaps. In the final phase, it also reveals causality, predicts and classifies potential APT attack nodes, and provides a comprehensive framework that not only predicts potential threats, but also provides insight into the logical sequence of the attacker’s actions. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Cybersecurity)
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15 pages, 730 KiB  
Article
FLSAD: Defending Backdoor Attacks in Federated Learning via Self-Attention Distillation
by Lucheng Chen, Xiaoshuang Liu, Ailing Wang, Weiwei Zhai and Xiang Cheng
Symmetry 2024, 16(11), 1497; https://doi.org/10.3390/sym16111497 - 8 Nov 2024
Viewed by 509
Abstract
Federated Learning (FL), as a distributed machine learning framework, can effectively learn symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible to malicious backdoor attacks through attackers injecting triggers into the backdoored model, resulting in backdoor samples being misclassified as target [...] Read more.
Federated Learning (FL), as a distributed machine learning framework, can effectively learn symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible to malicious backdoor attacks through attackers injecting triggers into the backdoored model, resulting in backdoor samples being misclassified as target classes. Due to the stealthy nature of backdoor attacks in FL, it is difficult for users to discover the symmetric and asymmetric backdoor properties. Currently, backdoor defense methods in FL cause model performance degradation while reducing backdoors. In addition, some methods will assume the existence of clean samples, which does not match the realistic scenarios. To address such issues, we propose FLSAD, an effective backdoor defense method in FL via self-attention distillation. FLSAD can recover the triggers using an entropy maximization estimator. Based on the recovered triggers, we leverage the self-attention distillation to eliminate the backdoor. Compared with the baseline backdoor defense methods, FLSAD can reduce the success rates of different state-of-the-art backdoor attacks to 2% on four real-world datasets through extensive evaluation. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Cybersecurity)
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12 pages, 282 KiB  
Article
Asymmetric Cryptography Based on the Tropical Jones Matrix
by Huawei Huang, Weisha Kong and Ting Xu
Symmetry 2024, 16(4), 456; https://doi.org/10.3390/sym16040456 - 9 Apr 2024
Cited by 1 | Viewed by 1076
Abstract
In recent years, the tropical polynomial factorization problem, the tropical matrix decomposition problem, and the tropical multivariate quadratic equation solving problem have been proved to be NP-hard. Some asymmetric cryptographic systems based on tropical semirings have been proposed, but most of them are [...] Read more.
In recent years, the tropical polynomial factorization problem, the tropical matrix decomposition problem, and the tropical multivariate quadratic equation solving problem have been proved to be NP-hard. Some asymmetric cryptographic systems based on tropical semirings have been proposed, but most of them are insecure and have been successfully attacked. In this paper, a new key exchange protocol and a new encryption protocol are proposed based on the difficulty of finding the multiple exponentiation problem of the tropical Jones matrices. The analysis results indicate that our protocol can resist various existing attacks. The complexity of attacking an MEP by adversaries is raised due to the larger number of combinations in the tropical Jones matrices compared to regular matrix polynomials. Furthermore, the index semiring is the non-negative integer cyclic matrix semiring, leading to a higher efficiency in key generation. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Cybersecurity)
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