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Advanced Computer Security and Applied Cybersecurity

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1106

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, University of Brasília, Brasília 70910-900, Brazil
Interests: distributed systems; information security; network management; network security; network systems; open source software; wireless networks
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Brasília, Brasília 70910-900, Brazil
Interests: cyber security; cyber intelligence; information security

E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues, 

This Special Issue is dedicated to advanced computer security and applied cybersecurity from the computer science, computer networks, and computer engineering perspective. Computer security and cybersecurity are nowadays transversal to multiple sectors and domains. So, solutions, methods, techniques, and development of technologies should evolve and adapt to the constant changes in cyberspace considering security and threats. Security researchers are required to create new solutions to address security challenges in this changing environment and combat advanced attacks on critical systems. Research and develop advances are expected to present security solutions for the society, industry, and organizations. Technology in this field is in constant evolution and so should cybersecurity considering artificial intelligence, cloud security, distributed systems, data security, privacy, internet of things, mobile systems, and other aspects. This Special Issue welcomes a range of contributions and encourages the submission of research papers for consideration. Indeed, we will consider paper submissions on practical security, applied cybersecurity, computer security and network security, linking theory with practice, and extending our understanding of cybersecurity and computer security.  

Topics of interest include but are not limited to the following: 

  • Computer network security;
  • Mitigation of distributed denial of service attacks;
  • Malware research;
  • Natural language processing applications for cybersecurity;
  • Machine learning applications for cybersecurity;
  • Cybersecurity for critical infrastructure; 
  • Cybersecurity applications for cybercrime prevention;
  • Internet of Things cybersecurity; 
  • Cloud security;
  • Privacy; 
  • Anonymity; 
  • DNS security; 
  • Supply chain security; 
  • DevSecOps; 
  • Advances in computer security; 
  • Protocol exploitation and mitigation; 
  • Anomaly detection; 
  • Authentication and authorization techniques; 
  • Zero-day attacks; 
  • Cyber intelligence; 
  • Cyber orchestration. 

Dr. Robson de Oliveira Albuquerque
Dr. João José Costa Gondim
Dr. Ana Lucila Sandoval Orozco
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. Applied Sciences 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

  • applied cybersecurity
  • computer science
  • computer networks
  • cyber intelligence
  • computer and network security

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Published Papers (1 paper)

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Research

24 pages, 1605 KiB  
Article
CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
by Shu Feng, Luhan Gao and Leyi Shi
Appl. Sci. 2025, 15(5), 2416; https://doi.org/10.3390/app15052416 - 24 Feb 2025
Viewed by 642
Abstract
The implementation of comprehensive security measures is a critical factor in the rapid growth of industrial control networks. Federated Learning has emerged as a viable solution for safeguarding privacy in machine learning. The effectiveness of pattern detection in models is diminished as a [...] Read more.
The implementation of comprehensive security measures is a critical factor in the rapid growth of industrial control networks. Federated Learning has emerged as a viable solution for safeguarding privacy in machine learning. The effectiveness of pattern detection in models is diminished as a result of the difficulty in extracting attack information from extremely large datasets and obtaining an adequate number of examples for specific types of attacks. A robust Federated Learning method, CGFL, is introduced in this study to resolve the challenges presented by data distribution discrepancies and client class imbalance. By employing a data generation strategy to generate balanced datasets for each client, CGFL enhances the global model. It employs a data generator that integrates artificially generated data with the existing data from local clients by employing label correction and data generation techniques. The geometric median aggregation technique was implemented to enhance the security of the aggregation process. The model was simulated and evaluated using the CIC-IDS2017 dataset, NSL-KDD dataset, and CSE-CIC-IDS2018 dataset. The experimental results indicate that CGFL does an effective job of enhancing the accuracy of ICS attack detection in Federated Learning under imbalanced sample conditions. Full article
(This article belongs to the Special Issue Advanced Computer Security and Applied Cybersecurity)
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