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Symmetry
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Applications Based on Symmetry in Adversarial Machine Learning
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Applications Based on Symmetry in Adversarial Machine Learning

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 2135

Special Issue Editors

Dr. Wanlun Ma

E-Mail Website
Guest Editor
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Australia
Interests: cybersecurity and privacy; machine learning; data management and data science
Dr. Yiliao Song

E-Mail Website
Guest Editor
School of Computer and Mathematical Sciences, University of Adelaide, Adelaide, Australia
Interests: trustworthy machine learning; data stream mining; real-time prediction
Dr. Xiao Chen

E-Mail Website
Guest Editor
School of Information and Physical Sciences, University of Newcastle, Newcastle, Australia
Interests: AI security; software and application security; data security and protection

Special Issue Information

Dear Colleagues,

Symmetry is a fundamental concept in adversarial machine learning that offers new opportunities to improve the robustness, security, and interpretability of modern learning systems. By harnessing symmetrical properties in data and model architectures, researchers can develop more effective defenses, identify vulnerabilities, and enhance the overall reliability of deep neural networks, large language models, and foundation models. As machine learning technologies increasingly permeate safety-critical applications, exploring symmetry in adversarial contexts is crucial for ensuring trustworthy and dependable AI systems.

This Special Issue, “Applications Based on Symmetry in Adversarial Machine Learning”, aims to bring together researchers and practitioners from diverse backgrounds to share their latest findings, methodologies, and advancements in this evolving field. We are particularly interested in studies that explore the interplay between symmetry and adversarial learning in areas such as computer vision, natural language processing, network security, autonomous systems, and large language models. We are soliciting contributions covering all related topics, including but not limited to cybersecurity, the Internet of Things, multimedia, networks, biometrics, behavior analysis, software engineering, digital health, simulation, and interdisciplinary applications. Both theoretical and application-oriented research articles are welcome.

We look forward to receiving your contributions.

Dr. Wanlun Ma
Dr. Yiliao Song
Dr. Xiao Chen
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 250 words) can be sent to the Editorial Office for assessment.

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

  • symmetry theories and applications
  • adversarial machine learning
  • adversarial attacks and defense
  • artificial intelligence
  • security, privacy and fairness

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

Published Papers (3 papers)

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Research

26 pages, 5213 KB  
Article
Design of Network Anomaly Detection Model Based on Graph Representation Learning
by Bo Qu, Simin Zheng, Junming Zeng and Liwei Tian
Symmetry 2025, 17(11), 1976; https://doi.org/10.3390/sym17111976 - 15 Nov 2025
Viewed by 381
Abstract
Network attacks are becoming increasingly diverse and sophisticated, resulting in complex cybersecurity challenges, which can be fundamentally viewed as a disruption of the symmetry or balanced state in normal network behavior. To address these challenges, graph representation learning methods have gained prominence in [...] Read more.
Network attacks are becoming increasingly diverse and sophisticated, resulting in complex cybersecurity challenges, which can be fundamentally viewed as a disruption of the symmetry or balanced state in normal network behavior. To address these challenges, graph representation learning methods have gained prominence in network anomaly detection. These methods effectively represent complex network traffic data as graphs and capture data relationships. By integrating deep learning, graph neural networks, and other techniques, graph representation learning enhances the accuracy and efficiency of network anomaly detection in complex network environments. This paper proposes a novel network anomaly detection model based on graph representation learning called ETG-EESAGE. The model constructs an event key time subgraph (ETG) to group similar data and enhance structural features. Then, it introduces an edge enhancement sampling aggregation algorithm (EESAGE) to capture node relations and differentiate edge information accurately. The model generates richer node feature representations during aggregation and detects abnormal nodes using a threshold. Experimental evaluations on the CIC-IDS2017 dataset demonstrate the strong performance of the proposed model across multiple daily subsets. Under optimal configuration settings, ETG-EESAGE achieves an average accuracy of 95.5%, precision of 97.9%, recall of 97.3%, and F1-score of 97.7%, outperforming other baseline algorithms. The model also exhibits strong interpretability and applicability in real-world network anomaly detection scenarios. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Adversarial Machine Learning)
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28 pages, 2340 KB  
Article
An Intelligent Playbook Recommendation Algorithm Based on Dynamic Interest Modeling for SOAR
by Hangyu Hu, Liangrui Zhang, Zhaoyu Zhang, Xingmiao Yao and Xia Wu
Symmetry 2025, 17(11), 1851; https://doi.org/10.3390/sym17111851 - 3 Nov 2025
Viewed by 514
Abstract
With the growing demand for refined security operations, Security Orchestration, Automation, and Response (SOAR) technologies have undergone rapid advancement. By leveraging intelligent orchestration capabilities in conjunction with core playbooks, SOAR facilitates both automated and semi-automated responses to security incidents. Nevertheless, the continuous evolution [...] Read more.
With the growing demand for refined security operations, Security Orchestration, Automation, and Response (SOAR) technologies have undergone rapid advancement. By leveraging intelligent orchestration capabilities in conjunction with core playbooks, SOAR facilitates both automated and semi-automated responses to security incidents. Nevertheless, the continuous evolution of network-attack techniques and the explosive growth of security alerts have rendered traditional static rule-based playbook matching and recommendation approaches increasingly inadequate in addressing the high frequency of alerts and the emergence of novel attack patterns. In this study, we propose an intelligent playbook recommendation algorithm for SOAR, developed under the paradigm of dynamic interest modeling. Specifically, the algorithm integrates a Transformer encoder, which captures long-term dynamic characteristics of alert signals in real time, with an LSTM network designed to extract short-term behavioral patterns. This hybrid architecture not only enables accurate playbook recommendations in high-volume alert scenarios, but also supports the reconstruction and optimization of playbooks, thereby offering valuable guidance for the mitigation of emerging threats. Experimental evaluations demonstrate that the proposed dynamic interest modeling-based algorithm exhibits high feasibility. It achieves improved performance in terms of both recommendation accuracy and efficiency, thus providing a robust technical foundation for enhancing the effectiveness of network security incident response and offering practical support for real-world security operations. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Adversarial Machine Learning)
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26 pages, 1325 KB  
Article
From Bribery–Stubborn Mining to Leading Hidden Triple-Fork Strategies for Incentive Optimization in PoW Blockchains
by Weijie Li, Shan Jiang, Bina Ni, Weipeng Liang and Yu Wang
Symmetry 2025, 17(10), 1618; https://doi.org/10.3390/sym17101618 - 30 Sep 2025
Viewed by 682
Abstract
Proof-of-Work (PoW) blockchains with symmetric consensus face threats such as selfish mining, bribery mining, block withholding, and replay attacks. This paper introduces a hybrid attack, Bribery–Stubborn Mining (BSbM), which integrates stubborn mining’s delayed chain publication with bribery incentives to recruit miners during forks. [...] Read more.
Proof-of-Work (PoW) blockchains with symmetric consensus face threats such as selfish mining, bribery mining, block withholding, and replay attacks. This paper introduces a hybrid attack, Bribery–Stubborn Mining (BSbM), which integrates stubborn mining’s delayed chain publication with bribery incentives to recruit miners during forks. Simulation experiments confirm that BSbM yields additional revenue. To obtain even higher revenue, we propose Leading Hidden Bribery–Stubborn Mining (LHBSbM) based on BSbM. By concealing and delaying broadcasts, LHBSbM constructs a triple fork, maintaining three chains at the same height. Upon revealing the private chain, two public blocks can be isolated, breaking the single-block limit of double-fork attacks. Theoretical analysis shows that LHBSbM raises the attacker’s maximum effective block rate from α/(1−α) to α/(1−α−β). Experimental results indicate that, under ideal conditions (r=0), BSbM becomes profitable once the attacker’s hash rate (α) exceeds approximately 34% and further confirm that, under certain conditions, LHBSbM nearly doubles isolated blocks compared to BSbM, yielding greater profits. Finally, potential defenses against such hybrid attacks are discussed, offering new insights for blockchain security. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Adversarial Machine Learning)
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