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Symmetry
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Information Security in AI
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Information Security in AI

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2787

Special Issue Editors

Dr. Jiageng Chen

E-Mail Website
Guest Editor
School of Computer Science, Central China Normal University, Wuhan 430070, China
Interests: symmetric/asymmetric ciphers; Blockchain; AI security
Dr. Zhe Xia

E-Mail Website
Guest Editor
School of Computer Science, Wuhan University of Technology, Wuhan 430070, China
Interests: cryptographic protocols; provable security; electronic voting
Special Issues, Collections and Topics in MDPI journals
Dr. Chunhua Su

E-Mail Website
Guest Editor
Department of Computer Science, University of Aizu, Fukushima 965-8580, Japan
Interests: information security; AI security; cryptographic protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the application of artificial intelligence (AI) technologies has become increasingly widespread in fields such as autonomous driving, industrial IoT, healthcare, and natural language processing. However, information security issues have become one of the key factors constraining the large-scale application of AI systems. For example, model training requires large amounts of data, but users often hesitate to share their data to protect their privacy, leading to the problem of data silos. This necessitates the development of methods that enable model training without compromising user privacy and also ensuring the trained models are privacy-preserving. Furthermore, using AI methods to identify security vulnerabilities and help enhance the security of information systems is also becoming an effective approach in designing secure systems.

The aim of this Special Issue is to bring together research investigations in identifying information security issues in AI systems, building secure and robust AI protocols and systems, and developing AI-based security enhancement techniques. Authors are invited to submit original research contributions on topics including but not limited to the following:

  • Data, model, and membership inference attacks;
  • Data and model poisoning attacks;
  • Adversarial training and certified robustness;
  • Privacy and security in federated learning;
  • Privacy-preserving computations;
  • Symmetric and asymmetric cryptographic protocols;
  • Key management through symmetric bivariate polynomials;
  • Information security in AI applications;
  • Security and privacy in AIGC;
  • AI-enabled digital investigation;
  • The integration of AI and Blockchain for security critical infrastructures.

Dr. Jiageng Chen
Dr. Zhe Xia
Dr. Chunhua Su
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.

Keywords

  • data, model, and membership inference attacks
  • data and model poisoning attacks
  • adversarial training and certified robustness
  • privacy and security in federated learning
  • privacy-preserving computations
  • symmetric and asymmetric cryptographic protocols
  • key management through symmetric bivariate polynomials
  • information security in AI applications
  • security and privacy in AIGC
  • AI-enabled digital investigation
  • the integration of AI and Blockchain for security critical infrastructures.

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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

19 pages, 767 KiB  
Article
Defending Graph Neural Networks Against Backdoor Attacks via Symmetry-Aware Graph Self-Distillation
by Hanlin Wang, Liang Wan and Xiao Yang
Symmetry 2025, 17(5), 735; https://doi.org/10.3390/sym17050735 - 10 May 2025
Viewed by 309
Abstract
Graph neural networks (GNNs) have exhibited remarkable performance in various applications. Still, research has revealed their vulnerability to backdoor attacks, where Adversaries inject malicious patterns during the training phase to establish a relationship between backdoor patterns and a specific target label, thereby manipulating [...] Read more.
Graph neural networks (GNNs) have exhibited remarkable performance in various applications. Still, research has revealed their vulnerability to backdoor attacks, where Adversaries inject malicious patterns during the training phase to establish a relationship between backdoor patterns and a specific target label, thereby manipulating the behavior of poisoned GNNs. The inherent symmetry present in the behavior of GNNs can be leveraged to strengthen the robustness of GNNs. This paper presents a quantitative metric, termed Logit Margin Rate (LMR), for analyzing the symmetric properties of the output landscapes across GNN layers. Additionally, a learning paradigm of graph self-distillation is combined with LMR to distill the symmetry knowledge from shallow layers, which can serve as the defensive supervision signals to preserve the benign symmetric relationships in deep layers, thus improving both model stability and adversarial robustness. Experiments were conducted on four benchmark datasets to evaluate the robustness of the proposed Graph Self-Distillation-based Backdoor Defense (GSD-BD) method against three widely used backdoor attack algorithms, demonstrating the robustness of GSD-BD even under severe infection scenarios. Full article
(This article belongs to the Special Issue Information Security in AI)
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28 pages, 1444 KiB  
Article
Enhancing Cryptocurrency Security: Leveraging Embeddings and Large Language Models for Creating Cryptocurrency Security Expert Systems
by Ahmed A. Abdallah, Heba K. Aslan, Mohamed S. Abdallah, Young-Im Cho and Marianne A. Azer
Symmetry 2025, 17(4), 496; https://doi.org/10.3390/sym17040496 - 26 Mar 2025
Viewed by 802
Abstract
In recent years, the rapid growth of cryptocurrency markets has highlighted the urgent need for advanced security solutions capable of addressing a spectrum of unique threats, from phishing and wallet hacks to complex blockchain vulnerabilities. This paper presents a comprehensive approach to fortifying [...] Read more.
In recent years, the rapid growth of cryptocurrency markets has highlighted the urgent need for advanced security solutions capable of addressing a spectrum of unique threats, from phishing and wallet hacks to complex blockchain vulnerabilities. This paper presents a comprehensive approach to fortifying cryptocurrency systems by harnessing the structural symmetry inherent in transactional patterns. By leveraging local large language models (LLMs), embeddings, and vector databases, we develop an intelligent and scalable security expert system that exploits symmetry-based anomaly detection to enhance threat identification. Cryptocurrency networks face increasing threats from sophisticated attacks that often exploit asymmetric vulnerabilities. To counteract these risks, we propose a novel security expert system that integrates symmetry-aware analysis through LLMs and advanced embedding techniques. Our system efficiently captures symmetrical transaction patterns, enabling robust detection of anomalies and threats while preserving structural integrity. By integrating a modular framework with LangChain and a vector database (Chroma DB), we achieve improved accuracy, recall, and precision by leveraging the symmetry of transaction distributions and behavioral patterns. This work sets a new benchmark for LLM-driven cybersecurity solutions, offering a scalable and adaptive approach to reinforcing the security symmetry in cryptocurrency systems. The proposed expert system was evaluated using a benchmark dataset of cryptocurrency transactions, including real-world threat scenarios involving phishing, fraudulent transactions, and blockchain anomalies. The system achieved an accuracy of 92%, a precision of 89%, and a recall of 93%, demonstrating a 10% improvement over existing security frameworks. Compared to traditional rule-based and machine learning-based detection methods, our approach significantly enhances real-time threat detection while reducing false positives. The integration of LLMs with embeddings and vector retrieval enables more efficient contextual anomaly detection, setting a new benchmark for AI-driven security solutions in the cryptocurrency domain. Full article
(This article belongs to the Special Issue Information Security in AI)
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14 pages, 3650 KiB  
Article
A Study on Network Anomaly Detection Using Fast Persistent Contrastive Divergence
by Jaeyeong Jeong, Seongmin Park, Joonhyung Lim, Jiwon Kang, Dongil Shin and Dongkyoo Shin
Symmetry 2024, 16(9), 1220; https://doi.org/10.3390/sym16091220 - 17 Sep 2024
Viewed by 1294
Abstract
As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these [...] Read more.
As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats. Full article
(This article belongs to the Special Issue Information Security in AI)
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