Novel Technologies for Systems and Network Security

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

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 2623

Special Issue Editors


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Guest Editor
College of Computer and Cyber Security and the Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350117, China
Interests: network security; big data; wireless communications; IoT; intelligent signal processing

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Guest Editor
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Interests: data security; multimedia content security; information hiding and digital watermarking; artificial intelligence security; intelligent fault detection; digital forensics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Soochow University, Suzhou 215301, China
Interests: intelligent security and trust provision for internet of things (IoT) networks; IoT data analytics and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The conventional technologies for system and network security are facing amount of challenges in six-generation (6G) network. This is mainly due to the open broadcast nature of radio signal, the dramatically increased computation capability of devices, and the high connections among systems, devices, machines, and people. Hence, new systems and network security methods are extremely important for 6G network, especially in the quantum age.

Technologies like machine and deep learning, physically unclonable functions, blockchain, and optimization methods can be considered to enhance the security of systems and network, such as the Internet-of-Things (IoT), Internet-of-Vehicles (IoV), Unmanned Aerial Vehicle (UAV) communication, and Satellite Communications (SATCOM) systems, just to name a few. How to enhance the security of these systems, while guaranteeing their communications is an open issue. Moreover, how to overcome the challenges brought by the quantum computer is required to be carefully considered.

The overarching aim of this special issue (SI) is to bring together leading researchers in both academia and industry from diverse backgrounds to advance the systems and network security,  as well as to consider their potential practical applications. Suitable topics for this SI include, but are not limited to, the following areas:

  • Machine and deep learning algorithms for system and network security;
  • Blockchain technique for system and network security;
  • Physical layer security;
  • AI-driven data analysis;
  • Joint optimization of security and communication;
  • Data security and privacy;
  • Online social network security, privacy and trust;
  • Security of communication protocols;
  • Cloud computing and virtualization security;
  • Biometrics security and privacy;
  • Access control and authorization;
  • Fault diagnosis and fault tolerance;
  • Information dissemination and control;
  • Multimedia content security;
  • Information hiding and digital watermarking;
  • Artificial intelligence security;
  • Deepfakes and detection techniques;
  • Digital forensics.

Prof. Dr. Li Xu
Prof. Dr. Hui Tian
Prof. Dr. He Fang
Guest Editors

Manuscript Submission Information

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Keywords

  • wireless communication and network security
  • AI
  • blockchain
  • optimization

Published Papers (1 paper)

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Research

12 pages, 464 KiB  
Communication
FedSpy: A Secure Collaborative Speech Steganalysis Framework Based on Federated Learning
by Hui Tian, Huidong Wang, Hanyu Quan, Wojciech Mazurczyk and Chin-Chen Chang
Electronics 2023, 12(13), 2854; https://doi.org/10.3390/electronics12132854 - 28 Jun 2023
Viewed by 1107
Abstract
Deep learning brings the opportunity to achieve effective speech steganalysis in speech signals. However, the speech samples used to train speech steganalysis models (i.e., steganalyzers) are usually sensitive and distributed among different agencies, making it impractical to train an effective centralized steganalyzer. Therefore, [...] Read more.
Deep learning brings the opportunity to achieve effective speech steganalysis in speech signals. However, the speech samples used to train speech steganalysis models (i.e., steganalyzers) are usually sensitive and distributed among different agencies, making it impractical to train an effective centralized steganalyzer. Therefore, in this paper, we present an effective framework, named FedSpy, using federated learning, which enables multiple agencies to securely and jointly train the speech steganalysis models without sharing their speech samples. FedSpy is a flexible and extensible framework that can work effectively in conjunction with various deep-learning-based speech steganalysis methods. We evaluate the performance of FedSpy by detecting the most widely used Quantization Index Modulation-based speech steganography with three state-of-the-art deep-learning-based steganalysis methods representatively. The results show that FedSpy significantly outperforms the local steganalyzers and achieves good detection accuracy comparable to the centralized steganalyzer. Full article
(This article belongs to the Special Issue Novel Technologies for Systems and Network Security)
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