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Next Generation Networks and Systems Security
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Next Generation Networks and Systems Security

A special issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1337

Special Issue Editors

Dr. Konstantinos Limniotis

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
Interests: cryptography; privacy; network security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicholas Kolokotronis

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of the Peloponnese, 221 31 Tripoli, Greece
Interests: cyber-security; game-theoretic security; autonomous security; privacy; risk management; cryptography; blockchain; post-quantum cryptography; coding theory; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Stavros Shiaeles

E-Mail Website
Guest Editor
Cyber Security Research Group, School of Computing University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: AI cybersecurity; intrusion detection and response; botnets; malware analysis; forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce this Special Issue on “Next Generation Networks and Systems Security”.

This Special Issue aims to present new research, as well as to highlight the latest results and current challenges, with respect to next-generation networks and systems security and privacy. In a highly evolving environment, where different technologies such as IoT networks, blockchain technologies, 5G Networks, Wi-Fi networks, cloud computing, etc. are being interconnected and expanded, new threats for security and privacy are constantly appearing. To address them, advanced technologies such as artificial intelligence, machine learning and deep learning, big data analysis, post-quantum cryptography, and privacy-enhancing technologies seem to have a crucial role.

This Special Issue seeks original unpublished papers in this area, focusing on novel system architecture designs, new detection techniques, new results on cryptography (including post-quantum cryptography as well as privacy-enhancing cryptography), either theoretical or practical, as well as on experimental studies\technical reports. Hardware attacks and defenses are also within the scope of the issue. Review/survey papers, as well as papers focusing on addressing/analyzing relevant legal requirements on cybersecurity and privacy, from an engineering perspective, are also highly encouraged.

You may choose our Joint Special Issue in Electronics.   

Dr. Konstantinos Limniotis
Dr. Nicholas Kolokotronis
Dr. Stavros Shiaeles
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. Network is an international peer-reviewed open access quarterly 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 1000 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

  • artificial intelligence
  • machine learning
  • deep learning
  • big data analysis
  • blockchain
  • cloud computing
  • cryptography
  • Internet of Things
  • intrusion detection
  • privacy
  • personal data protection
  • smart networks
  • 5G networks
  • Wi-Fi
  • IEEE 802.11 family of standards

Published Papers (1 paper)

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Research

23 pages, 625 KiB  
Article
Data Protection Issues in Automated Decision-Making Systems Based on Machine Learning: Research Challenges
by Paraskevi Christodoulou and Konstantinos Limniotis
Network 2024, 4(1), 91-113; https://doi.org/10.3390/network4010005 - 01 Mar 2024
Viewed by 687
Abstract
Data protection issues stemming from the use of machine learning algorithms that are used in automated decision-making systems are discussed in this paper. More precisely, the main challenges in this area are presented, putting emphasis on how important it is to simultaneously ensure [...] Read more.
Data protection issues stemming from the use of machine learning algorithms that are used in automated decision-making systems are discussed in this paper. More precisely, the main challenges in this area are presented, putting emphasis on how important it is to simultaneously ensure the accuracy of the algorithms as well as privacy and personal data protection for the individuals whose data are used for training the corresponding models. In this respect, we also discuss how specific well-known data protection attacks that can be mounted in processes based on such algorithms are associated with a lack of specific legal safeguards; to this end, the General Data Protection Regulation (GDPR) is used as the basis for our evaluation. In relation to these attacks, some important privacy-enhancing techniques in this field are also surveyed. Moreover, focusing explicitly on deep learning algorithms as a type of machine learning algorithm, we further elaborate on one such privacy-enhancing technique, namely, the application of differential privacy to the training dataset. In this respect, we present, through an extensive set of experiments, the main difficulties that occur if one needs to demonstrate that such a privacy-enhancing technique is, indeed, sufficient to mitigate all the risks for the fundamental rights of individuals. More precisely, although we manage—by the proper configuration of several algorithms’ parameters—to achieve accuracy at about 90% for specific privacy thresholds, it becomes evident that even these values for accuracy and privacy may be unacceptable if a deep learning algorithm is to be used for making decisions concerning individuals. The paper concludes with a discussion of the current challenges and future steps, both from a legal as well as from a technical perspective. Full article
(This article belongs to the Special Issue Next Generation Networks and Systems Security)
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