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Intelligent Detection Methods for Cybersecurity in Healthcare

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 12810

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Special Issue Editors

Dr. Faisal Saeed

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Guest Editor

School of Computing and Digital Technology, Birmingham City University, Birmingham, UK
Interests: data mining; artificial intelligence; machine learning; information retrieval; health informatics
Special Issues, Collections and Topics in MDPI journals

Dr. Tawfik Al-Hadhrami

E-Mail Website
Guest Editor

School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: internet of things; industrial control systems (ICS); cyber physical system (CPS); cyber security; wireless communications; smart cities; IoT health applications
Special Issues, Collections and Topics in MDPI journals

Dr. Fuad A. Ghaleb

E-Mail Website
Guest Editor

Department of Computer Science, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Interests: vehicular network security; cyber threat intelligence; intrusion detection; data science; data mining; knowledge discovery

Dr. Mohammed Al-Sarem

E-Mail Website
Guest Editor

College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia
Interests: data mining; machine learning and information retrieval

Special Issue Information

Dear Colleagues,

In healthcare organizations, the protection of patients’ information is very important. Huge amounts of data, which are sensitive and critical, are generated daily by several health applications such as practice management support systems, e-prescribing systems, clinical decision support systems, and radiology information systems. At the same time, these data are vulnerable to several types of attacks, such as phishing, ransomware and other malware. It is difficult for healthcare organisations to totally protect their digital assets against these attacks, as new cybersecurity attacks are periodically introduced. Therefore, it is necessary to provide intelligent solutions in response to these threats and to mitigate the security risks in the healthcare organisations.

One of these threats is the phishing, which is one of the serious issues that the cyber-world can face and has a dangerous impact on healthcare industries. One of the main challenges here is to enhance the detection rate of the phishing attacks. In the literature, there are many proposed methods for detecting phishing websites that appear similar to the target legitimate ones. However, more attention is required to propose more effective and efficient solutions to improve this detection.

This Special Issue will focus on several advances of cybersecurity in healthcare, which include:

AI based detection of cyber threats in smart healthcare;
IoT based systems for healthcare applications;
Phishing detection for healthcare data;
Security and privacy of healthcare data;
Medical fraud detection;
Smart health;
Health data science;
AI for clinical diagnostics;
Machine learning for healthcare;
Big data in healthcare;
Social media analytics for healthcare;
Future challenges of cybersecurity in healthcare;
Cybersecurity ICS;
Cybersecurity smart grid;
Misbehaviour detection;
IoT intrusion detection.

Dr. Faisal Saeed
Dr. Tawfik Al-Hadhrami
Dr. Fuad A. Ghaleb
Dr. Mohammed Al-Sarem
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

smart healthcare
Internet of Things
big data
cybersecurity
artificial intelligence
machine learning
ICS and smart grid
intrusion detection
smart communication technology

Published Papers (3 papers)

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Research

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23 pages, 5398 KiB

Open AccessArticle

DenseNet-201 and Xception Pre-Trained Deep Learning Models for Fruit Recognition

by Farsana Salim, Faisal Saeed, Shadi Basurra, Sultan Noman Qasem and Tawfik Al-Hadhrami

Electronics 2023, 12(14), 3132; https://doi.org/10.3390/electronics12143132 - 19 Jul 2023

Cited by 14 | Viewed by 3829

Abstract

With the dramatic increase of the global population and with food insecurity increasing, it has become a major concern for both individuals and governments to fulfill the need for foods such as vegetables and fruits. Moreover, the desire for the consumption of healthy food, including fruit, has increased the need for applications in the field of agriculture that help to achieve better methods for fruit sorting and fruit disease prediction and classification. Automated fruit recognition is a potential solution to reduce the time and labor required to identify different fruits in situations such as retail stores during checkout, fruit processing centers during sorting, and orchards during harvest. Automating these processes reduces the need for human intervention, making them cheaper, faster, and immune to human error and biases. Past research in the field has focused mainly on the size, shape, and color features of fruits or employed convolutional neural networks (CNNs) for their classification. This study investigates the effectiveness of pre-trained deep learning models for fruit classification using two distinct datasets: Fruits-360 and the Fruit Recognition dataset. Four pre-trained models, DenseNet-201, Xception, MobileNetV3-Small, and ResNet-50, were chosen for the experiments based on their architecture and features. The results show that all models achieved almost 99% accuracy or higher with Fruits-360. With the Fruit Recognition dataset, DenseNet-201 and Xception achieved accuracies of around 98%. The good results exhibited by DenseNet-201 and Xception on both the datasets are remarkable, with DenseNet-201 attaining accuracies of 99.87% and 98.94%, and Xception attaining 99.13% and 97.73% accuracy, respectively, on Fruits-360 and the Fruit Recognition dataset. Full article

(This article belongs to the Special Issue Intelligent Detection Methods for Cybersecurity in Healthcare)

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30 pages, 11930 KiB

Open AccessArticle

Efficient Biomedical Signal Security Algorithm for Smart Internet of Medical Things (IoMTs) Applications

by Achraf Daoui, Mohamed Yamni, Hicham Karmouni, Mhamed Sayyouri, Hassan Qjidaa, Saad Motahhir, Ouazzani Jamil, Walid El-Shafai, Abeer D. Algarni, Naglaa F. Soliman and Moustafa H. Aly

Electronics 2022, 11(23), 3867; https://doi.org/10.3390/electronics11233867 - 23 Nov 2022

Cited by 9 | Viewed by 2489

Abstract

Due to the rapid development of information and emerging communication technologies, developing and implementing solutions in the Internet of Medical Things (IoMTs) field have become relevant. This work developed a novel data security algorithm for deployment in emerging wireless biomedical sensor network (WBSN) and IoMTs applications while exchanging electronic patient folders (EPFs) over unsecured communication channels. These EPF data are collected using wireless biomedical sensors implemented in WBSN and IoMTs applications. Our algorithm is designed to ensure a high level of security for confidential patient information and verify the copyrights of bio-signal records included in the EPFs. The proposed scheme involves the use of Hahn’s discrete orthogonal moments for bio-signal feature vector extraction. Next, confidential patient information with the extracted feature vectors is converted into a QR code. The latter is then encrypted based on a proposed two-dimensional version of the modified chaotic logistic map. To demonstrate the feasibility of our scheme in IoMTs, it was implemented on a low-cost hardware board, namely Raspberry Pi, where the quad-core processors of this board are exploited using parallel computing. The conducted numerical experiments showed, on the one hand, that our scheme is highly secure and provides excellent robustness against common signal-processing attacks (noise, filtering, geometric transformations, compression, etc.). On the other hand, the obtained results demonstrated the fast running of our scheme when it is implemented on the Raspberry Pi board based on parallel computing. Furthermore, the results of the conducted comparisons reflect the superiority of our algorithm in terms of robustness when compared to recent bio-signal copyright protection schemes. Full article

(This article belongs to the Special Issue Intelligent Detection Methods for Cybersecurity in Healthcare)

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Review

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26 pages, 2283 KiB

Open AccessReview

A Systematic Literature Review on the Applications of Robots and Natural Language Processing in Education

by Hussain A. Younis, Nur Intan Raihana Ruhaiyem, Wad Ghaban, Nadhmi A. Gazem and Maged Nasser

Electronics 2023, 12(13), 2864; https://doi.org/10.3390/electronics12132864 - 28 Jun 2023

Cited by 9 | Viewed by 5168

Abstract

Natural language processing (NLP) is the art of investigating others’ positive and cooperative communication and rapprochement with others as well as the art of communicating and speaking with others. Furthermore, NLP techniques may substantially enhance most phases of the information-system lifecycle, facilitate access to information for users, and allow for new paradigms in the usage of information-system services. NLP also has an important role in designing the study, presenting two fields converging on one side and overlapping on the other, namely the field of the NAO-robot world and the field of education, technology, and progress. The selected articles classified the study into four categories: special needs, kindergartens, schools, and universities. Our study looked at accurate keyword research. They are artificial intelligence, learning and teaching, education, NAO robot, undergraduate students, and university. In two fields of twelve journals and citations on reliable/high-reputation scientific sites, 82 scientific articles were extracted. From the Scientific Journal Rankings (SJR) website, the study samples included twelve reliable/high-reputation scientific journals for the period from 2014 to 2023 from well-known scientific journals with a high impact factor. This study evaluated the effect of a systematic literature review of NAO educational robots on language programming. It aimed to be a platform and guide for researchers, interested persons, trainees, supervisors, students, and those interested in the fields of NAO robots and education. All studies recognized the superiority and progress of NAO robots in the educational field. They concluded by urging students to publish in highly influential journals with a high scientific impact within the two fields of study by focusing on the study-sample journals. Full article

(This article belongs to the Special Issue Intelligent Detection Methods for Cybersecurity in Healthcare)

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