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Security Technology-Driven Development for Sustainable Networks

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 10113

Special Issue Editors

School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: information security; data security; cyber security; privacy-enhancing technology
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Guest Editor
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Interests: cloud/edge computing; wireless communications; 5G/6G; IoT
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Guest Editor
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: cyber security; Internet of Things; smart infrastructures; machine learning

Special Issue Information

Dear Colleagues,

Due to the need to promote a sustainable society and economy, the concept of sustainable development has been continuously applied to the environment, energy, management and other social science fields. Due to the widespread nature of social media, the issue of the sustainable development of the network society is gradually receiving attention from all walks of life.

A network society is a "double-edged sword". Under the premise of effective governance and scientific control, it can benefit the public and promote the orderly development of society, otherwise it will harm public interest, hinder social development, and even endanger national security. Therefore, it is of great practical and strategic importance to promote the sustainable development of a network.

The sustainable development of a network society requires a high level of security for its network ecosystem, including, but not limited to, the protection of information transmission, privacy, property transaction security, etc. While ensuring that the legitimate rights and interests of people are better protected, better sustainable development can be achieved through a network society.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: sustainable development for blockchain security; AI security in sustainable networks; the protection of private information such as images and voice recordings; and sustainable data collection and data release.

We look forward to receiving your contributions.

Dr. Yi Sun
Dr. Limei Peng
Dr. Ali Kashif Bashir
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. Sustainability 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

  • sustainable development of network
  • blockchain security
  • AI security
  • image data security
  • voice data security
  • sustainable data collection, processing and transmission and security
  • sustainable data release

Published Papers (3 papers)

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Research

15 pages, 1505 KiB  
Article
Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks
by Ogobuchi Daniel Okey, Siti Sarah Maidin, Renata Lopes Rosa, Waqas Tariq Toor, Dick Carrillo Melgarejo, Lunchakorn Wuttisittikulkij, Muhammad Saadi and Demóstenes Zegarra Rodríguez
Sustainability 2022, 14(23), 15901; https://doi.org/10.3390/su142315901 - 29 Nov 2022
Cited by 11 | Viewed by 2286
Abstract
In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security [...] Read more.
In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol. Full article
(This article belongs to the Special Issue Security Technology-Driven Development for Sustainable Networks)
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19 pages, 4021 KiB  
Article
A Security Policy Protocol for Detection and Prevention of Internet Control Message Protocol Attacks in Software Defined Networks
by Edeh Michael Onyema, M. Anand Kumar, Sundaravadivazhagn Balasubaramanian, Salil Bharany, Ateeq Ur Rehman, Elsayed Tag Eldin and Muhammad Shafiq
Sustainability 2022, 14(19), 11950; https://doi.org/10.3390/su141911950 - 22 Sep 2022
Cited by 21 | Viewed by 2147
Abstract
Owing to the latest advancements in networking devices and functionalities, there is a need to build future intelligent networks that provide intellectualization, activation, and customization. Software-defined networks (SDN) are one of the latest and most trusted technologies that provide a method of network [...] Read more.
Owing to the latest advancements in networking devices and functionalities, there is a need to build future intelligent networks that provide intellectualization, activation, and customization. Software-defined networks (SDN) are one of the latest and most trusted technologies that provide a method of network management that provides network virtualization. Although traditional networks still have a strong presence in the industry, software-defined networks have begun to replace them at faster rates. When network technologies emerge at a steady rate, SDN will be implemented at higher rates in the upcoming years in all fields. Although SDN technology removes the complexity of tying control and data plane together over traditional networks, certain aspects such as security, controllability, and economy of network resources are vulnerable. Among these aspects, security is one of the main concerns that are to be viewed seriously as far as the applications of SDN are concerned. This paper presents the most recent security issues SDN environment followed by preventive mechanisms. This study focuses on Internet control message protocol (ICMP) attacks in SDN networks. This study proposes a security policy protocol (SPP) to detect attacks that target devices such as switches and the SDN controller in the SDN networks. The mechanism is based on ICMP attacks, which are the main source of flooding attacks in the SDN networks. The proposed model focuses on two aspects: security policy process verification and client authentication verification. Experimental results shows that the proposed model can effectively defend against flooding attacks in SDN network environments. Full article
(This article belongs to the Special Issue Security Technology-Driven Development for Sustainable Networks)
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21 pages, 738 KiB  
Article
XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems
by Thi-Thu-Huong Le, Yustus Eko Oktian and Howon Kim
Sustainability 2022, 14(14), 8707; https://doi.org/10.3390/su14148707 - 16 Jul 2022
Cited by 60 | Viewed by 4165
Abstract
The Industrial Internet of Things (IIoT) has advanced digital technology and the fastest interconnection, which creates opportunities to substantially grow industrial businesses today. Although IIoT provides promising opportunities for growth, the massive sensor IoT data collected are easily attacked by cyber criminals. Hence, [...] Read more.
The Industrial Internet of Things (IIoT) has advanced digital technology and the fastest interconnection, which creates opportunities to substantially grow industrial businesses today. Although IIoT provides promising opportunities for growth, the massive sensor IoT data collected are easily attacked by cyber criminals. Hence, IIoT requires different high security levels to protect the network. An Intrusion Detection System (IDS) is one of the crucial security solutions, which aims to detect the network’s abnormal behavior and monitor safe network traffic to avoid attacks. In particular, the effectiveness of the Machine Learning (ML)-based IDS approach to building a secure IDS application is attracting the security research community in both the general cyber network and the specific IIoT network. However, most available IIoT datasets contain multiclass output data with imbalanced distributions. This is the main reason for the reduction in the detection accuracy of attacks of the ML-based IDS model. This research proposes an IDS for IIoT imbalanced datasets by applying the eXtremely Gradient Boosting (XGBoost) model to overcome this issue. Two modern IIoT imbalanced datasets were used to assess our proposed method’s effectiveness and robustness, X-IIoTDS and TON_IoT. The XGBoost model achieved excellent attack detection with F1 scores of 99.9% and 99.87% on the two datasets. This result demonstrated that the proposed approach improved the detection attack performance in imbalanced multiclass IIoT datasets and was superior to existing IDS frameworks. Full article
(This article belongs to the Special Issue Security Technology-Driven Development for Sustainable Networks)
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