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Future Internet
Special Issues
5G Security: Challenges, Opportunities, and the Road Ahead
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Special Issue "5G Security: Challenges, Opportunities, and the Road Ahead"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 30 September 2023 | Viewed by 1851

Special Issue Editors

Dr. Mario Di Mauro

E-Mail Website
Guest Editor
Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
Interests: network management; network security; availability; 5G; NFV
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Pascale

E-Mail Website
Guest Editor
Accenture S.P.A., Via Sciangai 53, Roma, Italy
Interests: Internet of Things; cybersecurity; knowledge management; bayesian network; recommender systems; embedded systems
Dr. Marco Tambasco

E-Mail Website
Guest Editor
Ericsson Telecomunicazioni S.p.A., Via Filettine 89, 84016 Pagani, SA, Italy
Interests: networks analysis and design; network availability and performance; cybersecurity

Special Issue Information

Dear Colleagues,

Security aspects are becoming of crucial importance across the hyperconnected technological world. In this context, 5G (and its evolution, 6G) represents a key network enabler for a plethora of paradigms, including Internet of Things (IoT), cyber-physical systems (CPSs), multi-access edge computing (MEC), network function virtualization (NFV), software-defined networking (SDN), and many others.

Due to the growing interest both of academia and industry in the broad field of security, for this Special Issue we encourage high-quality research contributions—both theoretical and experimental—and timely survey papers that pinpoint future research directions in this field.

Finally, I would like to thank Mr. Giovanni Galatro and his valuable work for assisting me with this Special Issue.

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

  • Security protocols in 5G/6G architectures;
  • Privacy issues in 5G/6G architectures;
  • Security aspects in cloud/edge/fog computing;
  • Security aspects in multi-access edge computing (MEC);
  • Security aspects in the Internet of Things and/or cyber-physical systems;
  • Security management in modern virtualized networks (NFV, SDN, network slicing);
  • Access control mechanisms in modern networks;
  • Machine learning/artificial intelligence for 5G/6G network security;
  • Intrusion detection systems in 5G/6G networks;
  • Traffic analysis applied to 5G/6G networks;
  • Analytics and big data for network security;
  • Optimization techniques to improve 5G/6G network security;
  • Resilience strategies to improve 5G/6G network security;
  • Security aspects in 5G/6G vehicular communications;
  • Security aspects in millimeter-wave communications;
  • Security aspects in radio access networks (RANs);
  • Security aspects in 5G-oriented hardware (e.g., FPGA);
  • Security aspects in smart environments and industrial systems.

Dr. Mario Di Mauro
Dr. Marco Tambasco
Dr. Francesco Pascale
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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.

Published Papers (2 papers)

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Article
Enhancing Network Security: A Machine Learning-Based Approach for Detecting and Mitigating Krack and Kr00k Attacks in IEEE 802.11
by
Zaher Salah
and
Esraa Abu Elsoud
Future Internet 2023, 15(8), 269; https://doi.org/10.3390/fi15080269 - 14 Aug 2023
Viewed by 492
Abstract
The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal [...] Read more.
The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusions to guarantee data integrity and user privacy. In this situation, machine learning techniques used in data mining have proven to be effective tools for constructing intrusion detection systems (IDS) and improving their precision. We use the well-known AWID3 dataset, a comprehensive collection of wireless network traffic, to investigate the effectiveness of machine learning in enhancing network security. Our work primarily concentrates on Krack and Kr00k attacks, which target the most recent and dangerous flaws in IEEE 802.11 protocols. Through diligent implementation, we were able to successfully identify these threats using an IDS model that is based on machine learning. Notably, the resilience of our method was demonstrated by our ensemble classifier’s astounding 99% success rate in detecting the Krack attack. The effectiveness of our suggested remedy was further demonstrated by the high accuracy rate of 96.7% displayed by our neural network-based model in recognizing instances of the Kr00k attack. Our research shows the potential for considerably boosting network security in the face of new threats by leveraging the capabilities of machine learning and a diversified dataset. Our findings open the door for stronger, more proactive security measures to protect IEEE. 802.11 networks’ integrity, resulting in a safer online environment for all users. Full article
(This article belongs to the Special Issue 5G Security: Challenges, Opportunities, and the Road Ahead)
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Article
Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
by
Abdul Majeed
,
Abdullah M. Alnajim
,
Athar Waseem
,
Aleem Khaliq
,
Aqdas Naveed
,
Shabana Habib
,
Muhammad Islam
and
Sheroz Khan
Future Internet 2023, 15(6), 193; https://doi.org/10.3390/fi15060193 - 26 May 2023
Cited by 1 | Viewed by 819
Abstract
In fifth Generation (5G) networks, protection from internal attacks, external breaches, violation of confidentiality, and misuse of network vulnerabilities is a challenging task. Various approaches, especially deep-learning (DL) prototypes, have been adopted in order to counter such challenges. For 5G network defense, DL [...] Read more.
In fifth Generation (5G) networks, protection from internal attacks, external breaches, violation of confidentiality, and misuse of network vulnerabilities is a challenging task. Various approaches, especially deep-learning (DL) prototypes, have been adopted in order to counter such challenges. For 5G network defense, DL module are recommended here in order to symptomize suspicious NetFlow data. This module behaves as a virtual network function (VNF) and is placed along a 5G network. The DL module as a cyber threat-symptomizing (CTS) unit acts as a virtual security scanner along the 5G network data analytic function (NWDAF) to monitor the network data. When the data were found to be suspicious, causing network bottlenecks and let-downs of end-user services, they were labeled as “Anomalous”. For the best proactive and adaptive cyber defense system (PACDS), a logically organized modular approach has been followed to design the DL security module. In the application context, improvements have been made to input features dimension and computational complexity reduction with better response times and accuracy in outlier detection. Moreover, key performance indicators (KPIs) have been proposed for security module placement to secure interslice and intraslice communication channels from any internal or external attacks, also suggesting an adaptive defense mechanism and indicating its placement on a 5G network. Among the chosen DL models, the CNN model behaves as a stable model during behavior analysis in the results. The model classifies botnet-labeled data with 99.74% accuracy and higher precision. Full article
(This article belongs to the Special Issue 5G Security: Challenges, Opportunities, and the Road Ahead)
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