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Electronics
Special Issues
Network Security Management in Heterogeneous Networks
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Network Security Management in Heterogeneous Networks

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 1957

Special Issue Editors

Dr. Tao Zhang

E-Mail Website
Guest Editor
School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: network security; moving target defense; computer networks; federated learning
Dr. Xiangyun Tang

E-Mail Website
Guest Editor
School of Information Engineering, Minzu University of China, Beijing 100081, China
Interests: federated learning; AI Security; applied cryptography
Dr. Jiacheng Wang

E-Mail
Guest Editor
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: generative AI; semantic communication; resource allocation
Prof. Dr. Jiqiang Liu

E-Mail Website
Guest Editor
School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: trusted computing; privacy protection; cloud computing security

Special Issue Information

Dear Colleagues,

Recently, with the mutual connection and rapid development of diverse network devices, heterogeneous networks (e.g., mobile edge network and cloud-edge-terminal network) have emerged as a widespread network scenario. However, heterogeneous networks still face serious security challenges (e.g., DDoS, APT, and privacy leak), which will lead to network interruption and even more serious consequences. To handle this security problem, many methods have been proposed, such as moving target defense, blockchain, privacy protection, trusted computing, and malicious traffic detection. Therefore, the management of network security in heterogeneous networks can been regarded as an important research domain and still faces various challenges and new development trends.

The objective of this Special Issue is to explore recent advances that address the fundamental and practical challenges related to network security management in heterogeneous networks.

In this Special Issue, original research articles and reviews are welcome to be submitted. Research areas may include (but are not limited to) the following:

  • New security architectures for heterogeneous networks;
  • Secure resource allocation in mobile edge networks;
  • Security management for cloud-edge-terminal networks;
  • Moving target defense for heterogeneous networks;
  • Blockchain for heterogeneous networks;
  • Secure federated learning for heterogeneous networks;
  • Generative AI for network security management;
  • Privacy protection in mobile edge networks;
  • Trusted computing for heterogeneous networks;
  • Malicious traffic detection in cloud-edge-terminal networks.

We look forward to receiving your contributions.

Dr. Tao Zhang
Dr. Xiangyun Tang
Dr. Jiacheng Wang
Prof. Dr. Jiqiang Liu
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

  • heterogeneous network
  • moving target defense
  • blockchain
  • secure federated learning
  • generative AI
  • privacy protection
  • trusted computing
  • malicious traffic detection

Published Papers (2 papers)

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Research

19 pages, 729 KiB  
Article
CSIM: A Fast Community Detection Algorithm Based on Structure Information Maximization
by Yiwei Liu, Wencong Liu, Xiangyun Tang, Hao Yin, Peng Yin, Xin Xu and Yanbin Wang
Electronics 2024, 13(6), 1119; https://doi.org/10.3390/electronics13061119 - 19 Mar 2024
Viewed by 558
Abstract
Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters [...] Read more.
Community detection has been a subject of extensive research due to its broad applications across social media, computer science, biology, and complex systems. Modularity stands out as a predominant metric guiding community detection, with numerous algorithms aimed at maximizing modularity. However, modularity encounters a resolution limit problem when identifying small community structures. To tackle this challenge, this paper presents a novel approach by defining community structure information from the perspective of encoding edge information. This pioneering definition lays the foundation for the proposed fast community detection algorithm CSIM, boasting an average time complexity of only O(nlogn). Experimental results showcase that communities identified via the CSIM algorithm across various graph data types closely resemble ground truth community structures compared to those revealed via modularity-based algorithms. Furthermore, CSIM not only boasts lower time complexity than greedy algorithms optimizing community structure information but also achieves superior optimization results. Notably, in cyclic network graphs, CSIM surpasses modularity-based algorithms in effectively addressing the resolution limit problem. Full article
(This article belongs to the Special Issue Network Security Management in Heterogeneous Networks)
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13 pages, 26113 KiB  
Article
FedScrap: Layer-Wise Personalized Federated Learning for Scrap Detection
by Weidong Zhang, Dongshang Deng and Lidong Wang
Electronics 2024, 13(3), 527; https://doi.org/10.3390/electronics13030527 - 28 Jan 2024
Viewed by 762
Abstract
Scrap steel inspection is a critical entry point for connecting the smelting process to the industrial internet, with its security and privacy being of vital importance. Current advancements in scrap steel inspection involve collecting scattered data through the industrial internet, then utilizing them [...] Read more.
Scrap steel inspection is a critical entry point for connecting the smelting process to the industrial internet, with its security and privacy being of vital importance. Current advancements in scrap steel inspection involve collecting scattered data through the industrial internet, then utilizing them to train machine learning models for distributed classification. However, this detection method exposes original scrap steel data directly to the industrial internet, making it susceptible to interception by attackers, who can potentially obtain sensitive information. This paper presents a layer-wise personalized federated framework for scrap steel detection, termed FedScrap, which leverages federated learning (FL) to coordinate decentralized and heterogeneous scrap steel data while ensuring data privacy protection. The key challenge that FedScrap confronts is the heterogeneity of scrap steel data distributed across the network, which complicates the task of effectively integrating these data into a single detection model constructed via FL. To address this challenge, FedScrap employs a self-attention mechanism to aggregate personalized models for each layer of every client, focusing on the most relevant models to their specific data. By assigning higher attention scores to more relevant models, it achieves more accurate aggregation weights during the model aggregation process. To validate the efficacy of the proposed method, a dataset of scrap images was collected from a steel mill, and the results demonstrate that FedScrap achieves accurate classification of distributed scrap data with an impressive accuracy rate of 90%. Full article
(This article belongs to the Special Issue Network Security Management in Heterogeneous Networks)
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