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Electronics
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Advances in Information Processing and Network Security
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Advances in Information Processing and Network Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 3288

Special Issue Editors

Dr. Zhiyuan Zhang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: social computing; natural language processing; web data mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fei Xiong

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: web mining; social recommendation; complex networks and complex systems; AI applications
Dr. Yang Zhang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: decision making; information fusion; edge computing; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of information technology and artificial intelligence, an increasing number of individuals are gaining access to the Internet, bringing heightened attention to issues related to information processing and network security. Information processing techniques, such as information gathering, information storage, data mining, and information transmission, are being widely adopted. These techniques enable more efficient information analysis and decision-making across various domains. Furthermore, the growing complexity of modern network environments has led to an increasing need for advanced network security measures to protect sensitive information from cyber threats and privacy violations. For instance, in social networks, information processing technologies can identify and predict user behavior, detect anomalies, and visualize interactions to better understand social patterns, but during the information processing process, it is also necessary to pay attention to protecting user privacy and network attacks. Concurrently, advancements in network security are crucial in safeguarding data and ensuring privacy protection. Looking ahead, the integration of artificial intelligence, information processing, and network security is expected to play a pivotal role in information systems. AI-based technologies for information processing and network security provide essential tools for government and corporate decision-making while simultaneously enhancing public safety and operational efficiency. By securing sensitive data and ensuring reliable network infrastructure, they establish a solid foundation to address the challenges posed by today’s digital landscape.

The goal of this Special Issue of Electronics is to invite contributions focused on cutting-edge theories, methodologies, and applications related to information processing and network security. We encourage submissions that explore interdisciplinary approaches, advanced algorithms, and innovative applications across various social contexts.

Potential topics include, but are not limited to, the following:

  • Advances in neural information processing;
  • Cloud and edge computing and security;
  • Knowledge-based web data processing and mining;
  • Mobile crowd sensing and visualization;
  • Web vulnerability mining driven by artificial intelligence;
  • Information processing in IOT and industrial Internet;
  • Privacy protection on the Internet;
  • LLM-based information processing and its applications.

Dr. Zhiyuan Zhang
Prof. Dr. Fei Xiong
Dr. Yang Zhang
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. 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

  • neural information processing
  • data mining
  • network security
  • AI-based technologies

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

Published Papers (4 papers)

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Research

18 pages, 2181 KB  
Article
MPCTF: A Multi-Party Collaborative Training Framework for Large Language Models
by Ning Liu and Dan Liu
Electronics 2025, 14(16), 3253; https://doi.org/10.3390/electronics14163253 - 16 Aug 2025
Viewed by 426
Abstract
The demand for high-quality private data in large language models is growing significantly. However, private data is often scattered across different entities, leading to significant data silo issues. To alleviate such problems, we propose a novel multi-party collaborative training framework for large language [...] Read more.
The demand for high-quality private data in large language models is growing significantly. However, private data is often scattered across different entities, leading to significant data silo issues. To alleviate such problems, we propose a novel multi-party collaborative training framework for large language models, named MPCTF. MPCTF consists of several components to achieve multi-party collaborative training: (1) a one-click launch mechanism with multi-node and multi-GPU training capabilities, significantly simplifying user operations while enhancing automation and optimizing the collaborative training workflow; (2) four data partitioning strategies for splitting client datasets during the training process, namely fixed-size strategy, percentage-based strategy, maximum data volume strategy, and total data volume and available GPU memory strategy; (3) multiple aggregation strategies; and (4) multiple privacy protection strategies to achieve privacy protection. We conducted extensive experiments to validate the effectiveness of the proposed MPCTF. The experimental results demonstrate that the proposed MPCTF achieves superior performance; for example, our MPCTF acquired an accuracy rate of 65.43 and outperformed the existing work, which acquired an accuracy rate of 14.25 in the experiments. Moreover, we hope that our proposed MPCTF can promote the development of collaborative training for large language models. Full article
(This article belongs to the Special Issue Advances in Information Processing and Network Security)
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22 pages, 5378 KB  
Article
A Trustworthy Dataset for APT Intelligence with an Auto-Annotation Framework
by Rui Qi, Ga Xiang, Yangsen Zhang, Qunsheng Yang, Mingyue Cheng, Haoyang Zhang, Mingming Ma, Lu Sun and Zhixing Ma
Electronics 2025, 14(16), 3251; https://doi.org/10.3390/electronics14163251 - 15 Aug 2025
Viewed by 431
Abstract
Advanced Persistent Threats (APTs) pose significant cybersecurity challenges due to their multi-stage complexity. Knowledge graphs (KGs) effectively model APT attack processes through node-link architectures; however, the scarcity of high-quality, annotated datasets limits research progress. The primary challenge lies in balancing annotation cost and [...] Read more.
Advanced Persistent Threats (APTs) pose significant cybersecurity challenges due to their multi-stage complexity. Knowledge graphs (KGs) effectively model APT attack processes through node-link architectures; however, the scarcity of high-quality, annotated datasets limits research progress. The primary challenge lies in balancing annotation cost and quality, particularly due to the lack of quality assessment methods for graph annotation data. This study addresses these issues by extending existing APT ontology definitions and developing a dynamic, trustworthy annotation framework for APT knowledge graphs. The framework introduces a self-verification mechanism utilizing large language model (LLM) annotation consistency and establishes a comprehensive graph data metric system for problem localization in annotated data. This metric system, based on structural properties, logical consistency, and APT attack chain characteristics, comprehensively evaluates annotation quality across representation, syntax semantics, and topological structure. Experimental results show that this framework significantly reduces annotation costs while maintaining quality. Using this framework, we constructed LAPTKG, a reliable dataset containing over 10,000 entities and relations. Baseline evaluations show substantial improvements in entity and relation extraction performance after metric correction, validating the framework’s effectiveness in reliable APT knowledge graph dataset construction. Full article
(This article belongs to the Special Issue Advances in Information Processing and Network Security)
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21 pages, 5060 KB  
Article
Enhancing Mine Safety with YOLOv8-DBDC: Real-Time PPE Detection for Miners
by Jun Yang, Haizhen Xie, Xiaolan Zhang, Jiayue Chen and Shulong Sun
Electronics 2025, 14(14), 2788; https://doi.org/10.3390/electronics14142788 - 11 Jul 2025
Viewed by 652
Abstract
In the coal industry, miner safety is increasingly challenged by growing mining depths and complex environments. The failure to wear Personal Protective Equipment (PPE) is a frequent issue in accidents, threatening lives and reducing operational efficiency. Additionally, existing PPE datasets are inadequate for [...] Read more.
In the coal industry, miner safety is increasingly challenged by growing mining depths and complex environments. The failure to wear Personal Protective Equipment (PPE) is a frequent issue in accidents, threatening lives and reducing operational efficiency. Additionally, existing PPE datasets are inadequate for model training due to their small size, lack of diversity, and poor labeling. Current methods often struggle with the complexity of multi-scenario and multi-type PPE detection, especially under varying environmental conditions and with limited training data. In this paper, we propose a novel minersPPE dataset and an improved algorithm based on YOLOv8, enhanced with Dilated-CBAM (Dilated Convolutional Block Attention Module) and DBB (Diverse Branch Block) Detection Block (YOLOv8-DCDB), to address these challenges. The minersPPE dataset constructed in this paper includes 14 categories of protective equipment needed for various body parts of miners. To improve detection performance under complex lighting conditions and with varying PPE features, the algorithm incorporates the Dilated-CBAM module. Additionally, a multi-branch structured detection head is employed to effectively capture multi-scale features, especially enhancing the detection of small targets. To mitigate the class imbalance issue caused by the long-tail distribution in the dataset, we adopt a K-fold cross-validation strategy, optimizing the detection results. Compared to standard YOLOv8-based models, experiments on the minersPPE dataset demonstrate an 18.9% improvement in detection precision, verifying the effectiveness of the proposed YOLOv8-DCDB model in multi-scenario, multi-type PPE detection tasks. Full article
(This article belongs to the Special Issue Advances in Information Processing and Network Security)
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23 pages, 1250 KB  
Article
Spotting Sneaky Scammers: Malicious Account Detection from a Chinese Financial Platform
by Shunyu Yao, Dan Liu, Zhifei Guo, Zhiyuan Zhang and Jie Hu
Electronics 2024, 13(23), 4742; https://doi.org/10.3390/electronics13234742 - 29 Nov 2024
Viewed by 1156
Abstract
With the rapid development of e-commerce, malicious accounts have become a threat, especially in the business field. Therefore, how to efficiently detect malicious accounts has become one an important issue requiring resolution. Present research on malicious detection mainly uses the basic statistics of [...] Read more.
With the rapid development of e-commerce, malicious accounts have become a threat, especially in the business field. Therefore, how to efficiently detect malicious accounts has become one an important issue requiring resolution. Present research on malicious detection mainly uses the basic statistics of the original data to build models, and ignores malicious activities in the business field. To address the context-independent nature of business activities and their lack of social structure, this study constructs an online model for detecting malicious accounts in the business field based on a stacking ensemble strategy with BiGRU-Conv1D-Capsule, XGBoost, and LightGBM as individual learners and AdaBoost as a meta-learner. Experimental results show that the stacking ensemble model constructed in this study has better predictive power than the typical shallow learning and baseline deep learning models. In addition to the basic feature, the behavior sequence of users is also applied in the proposed model. Overall, our experiments based on a real dataset from a financial platform show that the TPR, AUC, and F1 of the stacking ensemble model can reach 0.8258, 0.9860, and 0.8922 respectively, which outperforms all baseline models. Full article
(This article belongs to the Special Issue Advances in Information Processing and Network Security)
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