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Advances in 5G Networks Security
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Advances in 5G Networks Security

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (1 March 2021) | Viewed by 11637

Special Issue Editors

Dr. Wojciech Mazurczyk

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Guest Editor
Warsaw University of Technology, Poland
Dr. Krzysztof Cabaj

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Guest Editor
Warsaw University of Technology, Poland

Special Issue Information

Dear Colleagues,

With the great success and development of 4G mobile networks, it is expected that the 5th generation wireless systems (in short 5G) will be a continued effort toward rich ubiquitous communication infrastructure, promising a wide range of high-quality services. It is envisioned that 5G communication will offer significantly greater data bandwidth and an almost infinite capability of networking resulting in unfaltering user experiences for, among others, virtual/augmented reality, massive content streaming, telepresence, user-centric computing, crowded area services, smart personal networks, Internet of Things (IoT), smart buildings, and smart cities.

5G communication is currently at the center of attention for industry, academia, and government worldwide. 5G drives many new requirements for different network capabilities. As 5G aims to utilize many promising network technologies, such as software-defined networking (SDN), network functions virtualization (NFV), information-centric network (ICN), network slicing, cloud computing, etc. and supporting a huge number of connected devices integrating advanced technologies and innovating new techniques will surely bring tremendous challenges for security, privacy, and trust. Therefore, secure network architectures, mechanisms, and protocols are required as the basis for 5G to address these issues and follow security-by-design approaches. Finally, since in 5G networks even more user data and network traffic will be transmitted, big data security solutions should be considered in order to address the magnitude of the data volume and ensure data security and privacy.

Dr. Wojciech Mazurczyk
Dr. Krzysztof Cabaj
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • 5G networks
  • information security
  • cybersecurity
  • IoT security
  • sensor networks
  • SDN
  • NVF

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Published Papers (2 papers)

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Research

11 pages, 4727 KiB  
Communication
On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method
by Dayu Shi, Xun Zhang, Lina Shi, Andrei Vladimirescu, Wojciech Mazurczyk, Krzysztof Cabaj, Benjamin Meunier, Kareem Ali, John Cosmas and Yue Zhang
Sensors 2021, 21(4), 1515; https://doi.org/10.3390/s21041515 - 22 Feb 2021
Cited by 12 | Viewed by 3412
Abstract
In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification [...] Read more.
In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%. Full article
(This article belongs to the Special Issue Advances in 5G Networks Security)
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30 pages, 1028 KiB  
Article
Explainable Security in SDN-Based IoT Networks
by Alper Kaan Sarica and Pelin Angin
Sensors 2020, 20(24), 7326; https://doi.org/10.3390/s20247326 - 20 Dec 2020
Cited by 41 | Viewed by 6474
Abstract
The significant advances in wireless networks in the past decade have made a variety of Internet of Things (IoT) use cases possible, greatly facilitating many operations in our daily lives. IoT is only expected to grow with 5G and beyond networks, which will [...] Read more.
The significant advances in wireless networks in the past decade have made a variety of Internet of Things (IoT) use cases possible, greatly facilitating many operations in our daily lives. IoT is only expected to grow with 5G and beyond networks, which will primarily rely on software-defined networking (SDN) and network functions virtualization for achieving the promised quality of service. The prevalence of IoT and the large attack surface that it has created calls for SDN-based intelligent security solutions that achieve real-time, automated intrusion detection and mitigation. In this paper, we propose a real-time intrusion detection and mitigation solution for SDN, which aims to provide autonomous security in the high-traffic IoT networks of the 5G and beyond era, while achieving a high degree of interpretability by human experts. The proposed approach is built upon automated flow feature extraction and classification of flows while using random forest classifiers at the SDN application layer. We present an SDN-specific dataset that we generated for IoT and provide results on the accuracy of intrusion detection in addition to performance results in the presence and absence of our proposed security mechanism. The experimental results demonstrate that the proposed security approach is promising for achieving real-time, highly accurate detection and mitigation of attacks in SDN-managed IoT networks. Full article
(This article belongs to the Special Issue Advances in 5G Networks Security)
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