New Advances and Challenges in Communication Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 56083

Special Issue Editors


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Guest Editor
Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia
Interests: cybersecurity; distributed denial of service (DDoS) attacks; Artificial Intelligence; intrusion detection and protection; Internet of Things; digital forensics; applied machine learning; quantum communication; quantum key distribution
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Information and Communication Traffic, Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia
Interests: innovative communication ecosystem; digital forensic; communication security; Industry 4.0; machine learning; AI
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science, University College Dublin, 4 Dublin, Ireland
Interests: security protocols design and analysis; network security; attack detection and prevention; automated techniques for formal verification; security for internet of things; application of blockchain for information security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Transport and Traffic Engineering, Department of Telecommunication Traffic and Networks, University of Belgrade, 11000 Belgrade, Serbia
Interests: routing in communication networks; AI in telecommunications; optical networking; tele-traffic engineering; telecommunication networking for ITS applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Communication networks represent the foundation of today's digital service delivery for various users over the world. New digital services and user requirements resulted in the fast development of communication networks and the emergence of new concepts and improvements of existing technologies. How did the coronavirus pandemic affect the use and development of communication networks, what are the possibilities of using artificial intelligence and machine learning in planning, managing, and securing communication networks, and what are the potential of quantum communications? What new challenges are communication networks facing regarding cybersecurity, quality of services, speeds, and capacities impacted by IoT devices?  We are planning to unite those, and other, research questions related to communication networks in this Special Issues entitled “New advances and challenges in communication networks”.

This Special Issue calls for papers presenting novel works regarding planning, development, optimizing, simulating, and securing communication networks. Furthermore, high-quality review and survey papers are welcomed. The papers considered for possible publication may focus on, but are not necessarily limited to, the following areas:

Keywords

  • Cybersecurity, Networks' Attack Detection and Prevention, and Digital forensics
  • Critical services communication
  • Quality of service/experience
  • Quantum communications
  • Predictions and classifications in network communications
  • Application of ML and AI in network communication
  • Innovative information and communication services
  • Security Protocols Design and Analysis and Formal  Verification 
  • Optimization of communication networks
  • Advanced technologies and techniques in optical networking
  • Network and traffic engineering in communication networks

Dr. Ivan Cvitić
Prof. Dr. Dragan Peraković
Prof. Dr. Anca Delia Jurcut
Prof. Dr. Goran Marković
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

  • Cybersecurity, Networks' Attack Detection and Prevention, and Digital forensics
  • Critical services communication
  • Quality of service/experience
  • Quantum communications
  • Predictions and classifications in network communications
  • Application of ML and AI in network communication
  • Innovative information and communication services
  • Security Protocols Design and Analysis and Formal Verification
  • Optimization of communication networks
  • Advanced technologies and techniques in optical networking
  • Network and traffic engineering in communication networks

Related Special Issue

Published Papers (6 papers)

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Research

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25 pages, 1469 KiB  
Article
Swarm Optimization and Machine Learning Applied to PE Malware Detection towards Cyber Threat Intelligence
by Santosh Jhansi Kattamuri, Ravi Kiran Varma Penmatsa, Sujata Chakravarty and Venkata Sai Pavan Madabathula
Electronics 2023, 12(2), 342; https://doi.org/10.3390/electronics12020342 - 9 Jan 2023
Cited by 7 | Viewed by 2621
Abstract
Cyber threat intelligence includes analysis of applications and their metadata for potential threats. Static malware detection of Windows executable files can be done through the analysis of Portable Executable (PE) application file headers. Benchmark datasets are available with PE file attributes; however, there [...] Read more.
Cyber threat intelligence includes analysis of applications and their metadata for potential threats. Static malware detection of Windows executable files can be done through the analysis of Portable Executable (PE) application file headers. Benchmark datasets are available with PE file attributes; however, there is scope for updating the data and also to research novel attribute reduction and performance improvement algorithms. The existing benchmark dataset contains non-PE header attributes, and few ignored attributes. In this work, a critical analysis was conducted to develop a new dataset called SOMLAP (Swarm Optimization and Machine Learning Applied to PE Malware Detection) with a value addition to the existing benchmark dataset. The SOMLAP data contains 51,409 samples that include both benign and malware files, with a total of 108 pure PE file header attributes. Further research was carried out to improve the performance of the Malware Detection System (MDS) by feature minimization using swarm optimization tools, viz., Ant Colony Optimization (ACO), Cuckoo Search Optimization (CSO), and Grey Wolf Optimization (GWO) wrapped with machine learning tools. The dataset was evaluated, and an accuracy of 99.37% with an optimized set of 12 features (ACO) proves the efficiency of the dataset, its attributes, and the algorithms used. Full article
(This article belongs to the Special Issue New Advances and Challenges in Communication Networks)
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26 pages, 7643 KiB  
Article
A New Scheme for Ransomware Classification and Clustering Using Static Features
by Bahaa Yamany, Mahmoud Said Elsayed, Anca D. Jurcut, Nashwa Abdelbaki and Marianne A. Azer
Electronics 2022, 11(20), 3307; https://doi.org/10.3390/electronics11203307 - 14 Oct 2022
Cited by 10 | Viewed by 3142
Abstract
Ransomware is a strain of malware that disables access to the user’s resources after infiltrating a victim’s system. Ransomware is one of the most dangerous malware organizations face by blocking data access or publishing private data over the internet. The major challenge of [...] Read more.
Ransomware is a strain of malware that disables access to the user’s resources after infiltrating a victim’s system. Ransomware is one of the most dangerous malware organizations face by blocking data access or publishing private data over the internet. The major challenge of any entity is how to decrypt the files encrypted by ransomware. Ransomware’s binary analysis can provide a means to characterize the relationships between different features used by ransomware families to track the ransomware encryption mechanism routine. In this paper, we compare the different ransomware detection approaches and techniques. We investigate the criteria, parameters, and tools used in the ransomware detection ecosystem. We present the main recommendations and best practices for ransomware mitigation. In addition, we propose an efficient ransomware indexing system that provides search functionalities, similarity checking, sample classification, and clustering. The new system scheme mainly targets native ransomware binaries, and the indexing engine depends on hybrid data from the static analyzer system. Our scheme tracks and classifies ransomware based on static features to find the similarity between different ransomware samples. This is done by calculating the absolute Jaccard index. Results have shown that Import Address Table (IAT) feature can be used to classify different ransomware more accurately than the Strings feature. Full article
(This article belongs to the Special Issue New Advances and Challenges in Communication Networks)
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18 pages, 449 KiB  
Article
A Review on Autonomous Vehicles: Progress, Methods and Challenges
by Darsh Parekh, Nishi Poddar, Aakash Rajpurkar, Manisha Chahal, Neeraj Kumar, Gyanendra Prasad Joshi and Woong Cho
Electronics 2022, 11(14), 2162; https://doi.org/10.3390/electronics11142162 - 11 Jul 2022
Cited by 118 | Viewed by 37990
Abstract
Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, [...] Read more.
Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities. Full article
(This article belongs to the Special Issue New Advances and Challenges in Communication Networks)
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17 pages, 4017 KiB  
Article
High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks
by Qasem Abu Al-Haija and Abdulaziz A. Alsulami
Electronics 2021, 10(17), 2113; https://doi.org/10.3390/electronics10172113 - 31 Aug 2021
Cited by 32 | Viewed by 4000
Abstract
The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have [...] Read more.
The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have exhibited high efficiency in the financial transaction systems, their payment transactions are vulnerable to several ransomware attacks. For that reason, investigators have been working on developing ransomware payment identification techniques for bitcoin transactions’ networks to prevent such harmful cyberattacks. In this paper, we propose a high performance Bitcoin transaction predictive system that investigates the Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks. Specifically, our system makes use of two supervised machine learning methods to learn the distinguishing patterns in Bitcoin payment transactions, namely, shallow neural networks (SNN) and optimizable decision trees (ODT). To validate the effectiveness of our solution approach, we evaluate our machine learning based predictive models on a recent Bitcoin transactions dataset in terms of classification accuracy as a key performance indicator and other key evaluation metrics such as the confusion matrix, positive predictive value, true positive rate, and the corresponding prediction errors. As a result, our superlative experimental result was registered to the model-based decision trees scoring 99.9% and 99.4% classification detection (two-class classifier) and accuracy (multiclass classifier), respectively. Hence, the obtained model accuracy results are superior as they surpassed many state-of-the-art models developed to identify ransomware payments in bitcoin transactions. Full article
(This article belongs to the Special Issue New Advances and Challenges in Communication Networks)
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14 pages, 1833 KiB  
Article
Derogation of Physical Layer Security Breaches in Maturing Heterogeneous Optical Networks
by Ammar Armghan
Electronics 2021, 10(16), 2021; https://doi.org/10.3390/electronics10162021 - 21 Aug 2021
Viewed by 1802
Abstract
The evolution journey of optical network (ON) towards heterogeneous and flexible frameworks with high order of applications is continued from the last decade. Furthermore, the prominence of optical security, amount of transmitted data, bandwidth, and dependable presentation are heightened. The performance of ON [...] Read more.
The evolution journey of optical network (ON) towards heterogeneous and flexible frameworks with high order of applications is continued from the last decade. Furthermore, the prominence of optical security, amount of transmitted data, bandwidth, and dependable presentation are heightened. The performance of ON is degraded in view of various natures of attacks at the physical layer, such as service disrupting and access to carrier data. In order to deal with such security breaches, new and efficient ON must be identified. So, this paper elaborates a detailed structure on physical layer security for heterogeneous ON. Possible mechanisms, such as Elliptic-curve Diffie–Hellman (ECDH), are used to treat a physical layer attack, and an efficient framework is proposed in this paper for 64 quadrature amplitude modulation-based orthogonal frequency division multiplex (64QAM-OFDM) ONs. Finally, theoretical and simulation validations are presented, and the effective results of the proposed method and viewpoint are concluded. Full article
(This article belongs to the Special Issue New Advances and Challenges in Communication Networks)
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Review

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19 pages, 449 KiB  
Review
On the Dependability of 6G Networks
by Ijaz Ahmad, Felipe Rodriguez, Jyrki Huusko and Kari Seppänen
Electronics 2023, 12(6), 1472; https://doi.org/10.3390/electronics12061472 - 20 Mar 2023
Cited by 5 | Viewed by 2656
Abstract
Sixth-generation communication networks must be highly dependable due to the foreseen connectivity of critical infrastructures through them. Dependability is a compound metric of four well-known concepts—reliability, availability, safety, and security. Each of these concepts have unique consequences for the success of 6G technologies [...] Read more.
Sixth-generation communication networks must be highly dependable due to the foreseen connectivity of critical infrastructures through them. Dependability is a compound metric of four well-known concepts—reliability, availability, safety, and security. Each of these concepts have unique consequences for the success of 6G technologies and applications. Using these concepts, we explore the dependability of 6G networks in this article. Due to the vital role of machine learning in 6G, the dependability of federated learning, as a distributed machine learning technique, has been studied. Since mission-critical applications (MCAs) are highly sensitive in nature, needing highly dependable connectivity, the dependability of MCAs in 6G is explored. Henceforth, this article provides important research directions to promote further research in strengthening the dependability of 6G networks. Full article
(This article belongs to the Special Issue New Advances and Challenges in Communication Networks)
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