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Electronics
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Advancements in Network and Data Security
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Advancements in Network and Data Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 753

Special Issue Editor

Dr. Jian Li

E-Mail Website
Guest Editor
1. Greenstone Software Co., Ltd., Beijing, China
2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: communication security; network security; data security; next-generation intra-/inter-vehicle communication technologies

Special Issue Information

Dear Colleagues,

With the widespread mobile internet, billions of people’s daily lives rely heavily on communication networks, AI technologies and remote data processing of huge data centres. While an increasing number of powerful applications make working and accessing entertainment significantly easier and more enjoyable, security problems underlying their key supporting technologies have grown more severe than ever before, threatening people’s assets and lives.

Therefore, in this Special Issue, we invite researchers to contribute original papers on current topics of interest in research on network and data security, including but not limited to the following:

  • 5G and IoT security;
  • Vehicular network security;
  • Satellite network security;
  • Distributed GPU/NPU network security;
  • RDMA (Remote Direct Memory Access) security;
  • AI-driven intrusion detection;
  • Zero-trust architectures;
  • Advanced encryption technologies;
  • Data breach prevention and responses;
  • Secure multi-party computation;
  • Privacy protection and data anonymization.

Dr. Jian Li
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. Electronics is an international peer-reviewed open access semimonthly 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

  • communication network security
  • data security
  • AI computing infrastructure security
  • advanced cryptographic technologies
  • AI-driven security mechanisms
  • remote computing security

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

Published Papers (2 papers)

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Research

22 pages, 4505 KiB  
Article
Advancing Secret Sharing in 3D Models Through Vertex Index Sharing
by Yuan-Yu Tsai, Jyun-Yu Jhou, Tz-Yi You and Ching-Ta Lu
Electronics 2025, 14(8), 1675; https://doi.org/10.3390/electronics14081675 - 21 Apr 2025
Viewed by 154
Abstract
Secret sharing is a robust data protection technique that secures sensitive information by partitioning it into multiple shares, such that the original data can only be reconstructed when a sufficient number of shares are combined. While this method has seen remarkable progress in [...] Read more.
Secret sharing is a robust data protection technique that secures sensitive information by partitioning it into multiple shares, such that the original data can only be reconstructed when a sufficient number of shares are combined. While this method has seen remarkable progress in the realm of images, its exploration and application in 3D models remain in their early stages. Given the growing prominence of 3D models in multimedia applications, ensuring their security and privacy has emerged as a critical area of research. At present, secret sharing approaches for 3D models predominantly rely on the vertex coordinates of the model as the basis for embedding and reconstructing secret messages. However, due to the limited quantity of vertex coordinates, these methods face significant constraints in embedding capacity, thereby limiting the potential of 3D models in secure data sharing. In contrast, the vertex indices of polygons, characterized by higher information density and greater structural flexibility, present a promising alternative medium for embedding secret shares. Building on this premise, the present study investigates the feasibility of leveraging shared vertex indices as a foundation for message embedding. It highlights the advantages of this approach in enhancing both the embedding capacity and the overall security of 3D models. By integrating the Chinese Remainder Theorem into vertex index-based sharing, the proposed method strengthens existing algorithms, offering improved model protection and enhanced embedding security. Experimental evaluations reveal that, compared to traditional vertex coordinate-based methods, incorporating vertex indices into secret sharing techniques significantly increases embedding efficiency while bolstering the security of 3D models. This study not only introduces an innovative approach to safeguarding 3D model data but also paves the way for the broader application of secret sharing techniques in the future. Full article
(This article belongs to the Special Issue Advancements in Network and Data Security)
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18 pages, 2911 KiB  
Article
ASIGM: An Innovative Adversarial Stego Image Generation Method for Fooling Convolutional Neural Network-Based Image Steganalysis Models
by Minji Kim, Youngho Cho, Hweerang Park and Gang Qu
Electronics 2025, 14(4), 764; https://doi.org/10.3390/electronics14040764 - 15 Feb 2025
Viewed by 473
Abstract
To defeat AI-based steganalysis systems, various techniques using adversarial example attack methods have been reported. In these techniques, adversarial stego images are generated using adversarial attack algorithms and steganography embedding algorithms sequentially and independently. However, this approach can be inefficient because both algorithms [...] Read more.
To defeat AI-based steganalysis systems, various techniques using adversarial example attack methods have been reported. In these techniques, adversarial stego images are generated using adversarial attack algorithms and steganography embedding algorithms sequentially and independently. However, this approach can be inefficient because both algorithms independently insert perturbations into a cover image, and the steganography embedding algorithm could significantly lower the undetectability or indistinguishability of adversarial attacks. To address this issue, we propose an innovative adversarial stego image generation method (ASIGM) that fully integrates the two separate algorithms by using the Jacobian-based Saliency Map Attack (JSMA). JSMA, one of the representative l0 norm-based adversarial example attack methods, is used to compute a set of pixels in the cover image that increases the probability of being classified as the non-stego class by the steganalysis model. The reason for this calculation is that if a secret message is inserted into the limited set of pixels in such a way, noise is only required for message embedding, and even misclassification of the target steganalysis model can be achieved without additional noise insertion. The experimental results demonstrate that our proposed ASIGM outperforms two representative steganography methods (WOW and ADS-WOW). Full article
(This article belongs to the Special Issue Advancements in Network and Data Security)
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