Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

► Journal Browser

Information, Cybersecurity and Modeling in Sustainable Future

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (29 March 2023) | Viewed by 15798

Share This Special Issue

Special Issue Editors

Dr. Ayman Radwan

E-Mail Website
Assistant Guest Editor

Instituto de Telecomunicações and Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: wireless communications; radio resource management; Internet of Things; future generations of mobile communications (5G)
Special Issues, Collections and Topics in MDPI journals

Dr. Hisham Almajed

E-Mail
Assistant Guest Editor

Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Interests: computer networks; modeling; cybersecurity

Prof. Dr. Ahmad Almogren

E-Mail Website
Chief Guest Editor

Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Interests: mobile and pervasive computing; computer security; sensor and cognitive networks; data consistency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainability and cybersecurity theory comprises the analysis and protection of sustainable systems, which can be approached from many different points of view. Novelty in cybersecurity builds theories represented by mathematical models that aim at understanding fundamental questions of systems’ spatial structure, self-organization, vulnerability, interaction, behavior, and development. Researchers, as a result, are increasingly facing the challenge of dealing with sustainability in advanced engineering applications and security that are based on efficient mathematical models. The topics that deal with these issues can be divided into three general parts: i) the development of general mathematical methods/models, ii) specific applications in particular domains, iii) cybersecurity assurance of the systems. The focus of this issue on the topics of information, cybersecurity and modeling sustainability is on mathematical models and engineering applications in sustainable systems. Moreover, we are interested in both new theoretical developments and studies regarding practical implementation concerning modeling, sustainability, attacks, and system vulnerability. The objective of this Special Issue is to identify, address and disseminate state-of-the-art research works on design, modeling, and cybersecurity. Prospective authors are invited to submit original contributions, including survey papers, for publication in this Special Issue.

Topics:

Sustainability in the Internet of Things (IoT)
Sustainability in Big Data
Sustainability and information security
Sustainability and advances in traditional systems
Forensic methods
Emerging approaches to cybersecurity and their sustainability
Incident response and sustainability in malware analysis
SCADA forensics, sustainability and critical infrastructure protection
Sustainability in digital forensic science
Cybercrime and model sustainability
Systems robustness control and sustainability
Sustainability in phishing and safety solutions

Prof. Ahmad Almogren
Dr. Ayman Radwan
Dr. Hisham Almajed
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

cybersecurity
IoT
models
sustainability

Published Papers (6 papers)

Download All Papers

Research

29 pages, 7108 KiB

Open AccessArticle

Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System

by Ali Alzahrani and Theyazn H. H. Aldhyani

Sustainability 2023, 15(10), 8076; https://0-doi-org.brum.beds.ac.uk/10.3390/su15108076 - 16 May 2023

Cited by 2 | Viewed by 2188

Abstract

Online food security and industrial environments and sustainability-related industries are highly confidential and in urgent need for network traffic analysis to attain proper security information to avoid attacks from anywhere in the world. The integration of cutting-edge technology such as the Internet of things (IoT) has resulted in a gradual increase in the number of vulnerabilities that may be exploited in supervisory control and data acquisition (SCADA) systems. In this research, we present a network intrusion detection system for SCADA networks that is based on deep learning. The goal of this system is to defend ICSs against network-based assaults that are both conventional and SCADA-specific. An empirical evaluation of a number of classification techniques including k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), convolution neural network (CNN), and integrated gated recurrent unit (GRU) is reported in this paper. The suggested algorithms were tested on a genuine industrial control system (SCADA), which was known as the WUSTL-IIoT-2018 and WUSTL-IIoT-20121 datasets. SCADA system operators are now able to augment proposed machine learning and deep learning models with site-specific network attack traces as a result of our invention of a re-training method to handle previously unforeseen instances of network attacks. The empirical results, using realistic SCADA traffic datasets, show that the proposed machine learning and deep-learning-based approach is well-suited for network intrusion detection in SCADA systems, achieving high detection accuracy and providing the capability to handle newly emerging threats. The accuracy performance attained by the KNN and RF algorithms was superior and achieved a near-perfect score of 99.99%, whereas the CNN-GRU model scored an accuracy of 99.98% using WUSTL-IIoT-2018. The Rf and GRU algorithms achieved >99.75% using the WUSTL-IIoT-20121 dataset. In addition, a statistical analysis method was developed in order to anticipate the error that exists between the target values and the prediction values. According to the findings of the statistical analysis, the KNN, RF, and CNN-GRU approaches were successful in achieving an R² > 99%. This was demonstrated by the fact that the approach was able to handle previously unknown threats in the industrial control systems (ICSs) environment. Full article

(This article belongs to the Special Issue Information, Cybersecurity and Modeling in Sustainable Future)

► Show Figures

Figure 1

23 pages, 2554 KiB

Open AccessArticle

Securing Access to Internet of Medical Things Using a Graphical-Password-Based User Authentication Scheme

by Mudassar Ali Khan, Ikram Ud Din and Ahmad Almogren

Sustainability 2023, 15(6), 5207; https://0-doi-org.brum.beds.ac.uk/10.3390/su15065207 - 15 Mar 2023

Cited by 4 | Viewed by 1947

Abstract

Digital healthcare services have seen significant growth in this decade and many new technologies have been thoroughly examined to provide efficient services through secure infrastructures. The Internet of Medical Things (IoMT) revitalizes a healthcare infrastructure by creating an interconnected, intelligent, accessible, and efficient network. While there have been many studies on possible device authentication techniques for the IoMT, there is still much work to be done in user authentication to provide sustainable IoT solutions. Graphical passwords, which use visual content such as images instead of traditional text-based passwords, can help users authenticate themselves. However, current schemes have limitations. Therefore, this paper proposes a novel graphical authentication scheme that uses multiple factors to register and authenticate users using simple arithmetic operations, machine learning for hand gesture recognition, and medical images for recall purposes. The proposed method is designed to keep the authentication process simple, memorable, and robust. To evaluate the proposed scheme, we use the Post-Study System Usability Questionnaire (PSSUQ) to compare it with PIN-based and pattern-based authentication techniques. While comparing treatment and comparison groups, system quality showed a 16.7% better score, information quality a 25% increase, interface quality a 40% increase, and overall quality showed a 25% increase. The proposed method successfully revitalizes the use of graphical passwords, specifically in the field of IoMT, by developing a user-friendly, satisfying, and robust authentication scheme. Full article

(This article belongs to the Special Issue Information, Cybersecurity and Modeling in Sustainable Future)

► Show Figures

Figure 1

19 pages, 3921 KiB

Open AccessArticle

The Impact of Environmental Pollution on Residents’ Income Caused by the Imbalance of Regional Economic Development Based on Artificial Intelligence

by Binfeng Ma

Sustainability 2023, 15(1), 637; https://0-doi-org.brum.beds.ac.uk/10.3390/su15010637 - 30 Dec 2022

Cited by 3 | Viewed by 1693

Abstract

Regional economy is a human economic activity in a geographical region, a prominent and sustainable economy with distinctive regional characteristics. Regional economy is characterized by its integrity, relativity, relative independence, and spatial difference. With the increasing development of science and technology and big data, it has become a normal trend to use artificial intelligence technology to solve current social problems. In this paper, the social problems caused by the imbalance of regional economy are analyzed based on artificial intelligence. Through the application of KNN-SVM algorithm optimization, it is found that AI has relatively little impact on the development of the income level of the population under the age of 25 in each region. Compared with previous studies, the quality is compared with the innovation of this document, which is the development of a conceptual framework approach, an environmental pollution analysis mechanism, and income inequality analysis. The empirical research results show that under the strategic background of improving people’s livelihood, accelerating the reform of ecological civilization and promoting the construction of the health system, the relationship between environmental pollution and population income caused by unbalanced regional economic development can be re-analyzed through the best KNN-SVM algorithm. The implementation of the healthy China strategy has important theoretical and practical significance. Full article

(This article belongs to the Special Issue Information, Cybersecurity and Modeling in Sustainable Future)

► Show Figures

Figure 1

13 pages, 2378 KiB

Open AccessArticle

Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids

by Muqing Wu, Qingsu He, Yuping Liu, Ziqiang Zhang, Zhongwen Shi and Yifan He

Sustainability 2022, 14(21), 13939; https://0-doi-org.brum.beds.ac.uk/10.3390/su142113939 - 26 Oct 2022

Cited by 1 | Viewed by 1021

Abstract

Given the vitality of the renewable-energy grid market, the optimal allocation of clean energy is crucial. An optimal dispatching method for source–load coordination of renewable-energy grid is proposed. An improved K-means clustering algorithm is used to preprocess the source data and historical load data. A support vector machine is used to predict the cluster of renewable-energy grid resources and load data, and typical scenarios are selected from the prediction results. Taking typical scenarios as a representative, the probability distribution of wind power output is accurately obtained. An optimization model of the total operation cost of the renewable-energy grid is established. The experimental results show that the algorithm reduces the error between the predicted value and the actual value. Our method can improve the real-time prediction accuracy of the renewable-energy grid system and increase the economic benefits of the renewable energy grid. Full article

(This article belongs to the Special Issue Information, Cybersecurity and Modeling in Sustainable Future)

► Show Figures

Figure 1

18 pages, 2273 KiB

Open AccessArticle

The Impact of Urban Development on Wetland Conservation

by Zhaobin Li, Lei Ma, Dongmei Gou, Qianqian Hong, Leungkam Fai and Bo Xiong

Sustainability 2022, 14(21), 13747; https://0-doi-org.brum.beds.ac.uk/10.3390/su142113747 - 24 Oct 2022

Cited by 4 | Viewed by 4200

Abstract

Wetland is an integrated ecosystem which includes ecosystems such as hydrology, soil, vegetation, and biological environments. At present, the urbanization rate of China’s national economic development process is rapidly increasing, and by the end of 2021, the urbanization rate of China’s resident population will be 64.72%. This paper analyzes the hydrological effects of urbanization, the impact of water resources, climate change, and biodiversity on wetland ecosystems, and also analyzes the role of wetlands on the ecological environment, especially in terms of ecological and cultural values. The economic and social benefits of the whole society are also analyzed. The ecological and social benefits of urban wetlands have made their conservation and sustainable development increasingly important worldwide. Based on the current situation of China’s urban wetland protection and restoration, we put forward countermeasures and suggestions for China’s urban wetland protection. This is conducive to promoting the sustainable development of the urban wetland ecosystem, promoting the operation of the market, realizing the optimal allocation of ecological resources, improving the benefits of ecological environmental protection, and promoting the coordinated development of the ecological environment. This paper provides a reference for the better development of wetland conservation under urbanization development conditions. Full article

(This article belongs to the Special Issue Information, Cybersecurity and Modeling in Sustainable Future)

► Show Figures

Figure 1

16 pages, 1430 KiB

Open AccessArticle

HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning

by Amit Sundas, Sumit Badotra, Salil Bharany, Ahmad Almogren, Elsayed M. Tag-ElDin and Ateeq Ur Rehman

Sustainability 2022, 14(19), 11934; https://0-doi-org.brum.beds.ac.uk/10.3390/su141911934 - 22 Sep 2022

Cited by 18 | Viewed by 2656

Abstract

Utilization of the Internet of Things and ubiquitous computing in medical apparatuses have “smartified” the current healthcare system. These days, healthcare is used for more than simply curing patients. A Smart Healthcare System (SHS) is a network of implanted medical devices and wearables that monitors patients in real-time to detect and avert potentially fatal illnesses. With its expanding capabilities comes a slew of security threats, and there are many ways in which a SHS might be exploited by malicious actors. These include, but are not limited to, interfering with regular SHS functioning, inserting bogus data to modify vital signs, and meddling with medical devices. This study presents HealthGuard, an innovative security architecture for SHSs that uses machine learning to identify potentially harmful actions taken by users. HealthGuard monitors the vitals of many SHS-connected devices and compares the vitals to distinguish normal from abnormal activity. For the purpose of locating potentially dangerous actions inside a SHS, HealthGuard employs four distinct machine learning-based detection approaches (Artificial Neural Network, Decision Tree, Random Forest, and k-Nearest Neighbor). Eight different smart medical devices were used to train HealthGuard for a total of twelve harmless occurrences, seven of which are common user activities and five of which are disease-related occurrences. HealthGuard was also tested for its ability to defend against three distinct forms of harmful attack. Our comprehensive analysis demonstrates that HealthGuard is a reliable security architecture for SHSs, with a 91% success rate and in F1-score of 90% success. Full article

(This article belongs to the Special Issue Information, Cybersecurity and Modeling in Sustainable Future)

► Show Figures