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Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous...
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Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 896

Special Issue Editors

Dr. Rameez Asif

E-Mail Website
Guest Editor
National Centre for Motorsport Engineering (NCME), Faculty of Engineering, University of Bolton, Bolton BL3 5AB, UK
Interests: artificial intelligence and robotics; microwave and wireless communications; signal processing; avionics communications; heterogeneous wireless networks; software defined radios; miniaturized transceiver design; meta-materials design; MU-massive MIMO
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Ali

E-Mail Website
Guest Editor
Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Interests: avionics communications networks; heterogeneous wireless networks; software defined networks; artificial intelligence; embedded systems; network security and desktop application development

Special Issue Information

Dear Colleagues,

The demand for the Always Best Connected (ABC) paradigm is evolving throughout the life cycle of communications and the digital entertainment industry. Today, with the sky-rocketing demands of digitally connected life globally, more bandwidth is needed than ever. The advent of technologies such as 4G, 5G and 6G is contributing considerably to fulfilling the data speed demand, but the extent of their coverage represents a barrier to these technologies. Several solutions have been proposed by researchers around the world to overcome the limitations of individual radio access technologies such as cognitive radios, collaborative radio resource management, heterogenous wireless communications networks etc., with the assistance of Artificial Intelligence (AI), machine learning (ML), and new technologies such as software-defined networking (SDN), etc. The amalgamation of different radio access technologies, AI/ML, SDN, and other new technologies introduces a large number of benefits but comes with the price of new cybersecurity vulnerabilities and new additions and upgrades in the architecture and protocol stacks.

This Special Issue aims to address issues that are involved in the analysis, design, and implementation of different communication layers featuring in a heterogeneous wireless network for seamless mobility, security, and resource allocation augmented with AI/ML, SDN, and other new technologies, including techniques that can help to secure this communication.

This includes:

  • Heterogeneous wireless networks;
  • Seamless mobility in heterogeneous wireless networks;
  • Satellite communications;
  • Vehicular communications networks based on software-defined networks;
  • AI/ML-assisted radio link selection;
  • Channel design and coding;
  • AI/ML-assisted cybersecurity for heterogeneous wireless networks;
  • Mobility protocols for fast moving vehicular communications networks;
  • SDN-assisted security architecture for heterogeneous wireless networks;
  • Link selection in multi-link node wireless networks;
  • Handovers in wireless networks;
  • Load balancing in wireless networks;
  • Network management;
  • Encryption techniques for transmitter and receiver design;
  • Cybersecurity.

Dr. Rameez Asif
Dr. Muhammad Ali
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. Electronics 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

  • encryption techniques
  • wireless networks
  • AI/ML
  • SDN
  • link selection

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Published Papers (1 paper)

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Research

18 pages, 1292 KiB  
Article
Network Attack Classification with a Shallow Neural Network for Internet and Internet of Things (IoT) Traffic
by Jörg Ehmer, Yvon Savaria, Bertrand Granado, Jean-Pierre David and Julien Denoulet
Electronics 2024, 13(16), 3318; https://doi.org/10.3390/electronics13163318 - 21 Aug 2024
Viewed by 431
Abstract
In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, [...] Read more.
In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, exposing industrial infrastructure to the global Internet also generates security challenges that need to be addressed to benefit from tighter systems integration and reduced reaction times. Machine learning algorithms have demonstrated their capacity to detect sophisticated cyber attack patterns. However, they often consume significant amounts of memory, computing resources, and scarce energy. Furthermore, their training relies on the availability of datasets that accurately represent real-world data traffic subject to cyber attacks. Network attacks are relatively rare events, as is reflected in the distribution of typical training datasets. Such imbalanced datasets can bias the training of a neural network and prevent it from successfully detecting underrepresented attack samples, generally known as the problem of imbalanced learning. This paper presents a shallow neural network comprising only 110 ReLU-activated artificial neurons capable of detecting representative attacks observed on a communication network. To enable the training of such small neural networks, we propose an improved attack-sharing loss function to cope with imbalanced learning. We demonstrate that our proposed solution can detect network attacks with an F1 score above 99% for various attacks found in current intrusion detection system datasets, focusing on IoT device communication. We further show that our solution can reduce the false negative detection rate of our proposed shallow network and thus further improve network security while enabling processing at line rate in low-complexity network intrusion systems. Full article
(This article belongs to the Special Issue Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks)
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