Special Issue "Recent Trends and Applications of Smart Systems for Cybersecurity"

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 2224

Special Issue Editors

College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Interests: cybersecurity; blockchain; machine and deep learning; software engineering; health informatics
Maersk Mc Kinney Moller Institute, University of Sothern Denmark, Campusvej 55, 5230 Odense M, Denmark
Interests: health informatics; security informatics; social network analysis and mining; hypermedia; data-driven health technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The cybersphere has significantly contributed to the advancement of science and technology in recent decades. However, the networks that link systems together, the availability of information, the variety of computer technology, and the combination of all these factors put the infrastructures of both the academic and industrial sectors at considerable risk. The potential for additional attacks and attackers is a natural outcome when more attack surfaces are made available, primarily resulting from unauthorized use or access to computerized platforms without the owner's consent. The results of these attacks are virtually always intrusive and disruptive for computerized systems, and they may even be lethal if they target smart medical equipment.

Therefore, it becomes necessary to investigate practical security-based solutions for eliminating these dangers or thwarting the attacks. The application of intelligent technologies, such as intelligent computing (IC) and its sub-domains (like artificial intelligence (AI)), has helped achieve this in recent years. These have offered reliable assistance in various fields, such as identifying cyber risks, evaluating software package risks, and maintaining the security of Internet connections. However, the intricacy of new attacks will be too much for current technology, protocols, and other antiquated countermeasures to handle. Thus, a rapid co-evolution of defenses that match the speed and sophistication of IC-based offensive approaches is necessary because new assault tools and plans can now be generated utilizing several machine- and deep-learning techniques. Nonetheless, if the security and ethical issues are not effectively handled, the adoption of IC in cybersecurity could be hindered or potentially cause serious issues for society. Due to this, there is a significant chance that scientific knowledge may be misused.

Therefore, the shared understanding of the latest cyber defense technology will aid in thwarting malicious users. Additionally, it will produce information on the techniques and tools used by hackers, establish uniform blacklists of suspected services that violate cyber security and privacy rules, and aid in determining whether an enterprise has enough defense resources. Given the significant academic and industrial implications of studying these topics for societally positive outcomes, this Special Issue aims to cover recent findings on theoretical advancements and industrial challenges that combine IC/AI methods with significant cybersecurity issues to underlie the construction of secure systems. Offensive and defensive machine learning security applications might be the subject of research articles.

  • The following list includes some potential areas of interest for this Special Issue;
  • Artificial intelligence (AI) forensics;
  • AI applications for security and privacy;
  • AI threat intelligence Intelligent systems for cryptography;
  • Intelligent biometrics and watermarking Scalable;
  • Intelligent computing (IC) for cloud services and security Security in wireless sensor networks.

Prof. Dr. Asadullah Shaikh
Dr. Jawad Rasheed
Dr. Hani Alshahrani
Prof. Dr. Uffe Kock Wiil
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 2200 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.


  • artificial intelligence
  • cyber security
  • AI threat intelligence
  • smart intrusion detection

Published Papers (1 paper)

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17 pages, 3180 KiB  
Resilient Security Framework Using TNN and Blockchain for IoMT
Electronics 2023, 12(10), 2252; https://doi.org/10.3390/electronics12102252 - 15 May 2023
Cited by 1 | Viewed by 1059
The growth of the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to various cybersecurity attacks and threats, which lead to negative consequences. Cyberattacks can damage [...] Read more.
The growth of the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to various cybersecurity attacks and threats, which lead to negative consequences. Cyberattacks can damage not just the IoMT devices in use but also human life. Currently, several security solutions have been proposed to enhance the security of the IoMT, employing machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. Furthermore, blockchain technology enables a decentralized approach to the healthcare system, eliminating some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects malicious data measured by medical sensors to find fraudulent data. As a result, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. Additionally, a blockchain network is used in the fog layer to ensure that the data is not altered, enhancing the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% based on the F1-score accuracy metric. Full article
(This article belongs to the Special Issue Recent Trends and Applications of Smart Systems for Cybersecurity)
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