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Electronics
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Editorial Board Members’ Collection Series: "AI for Cybersecurity"
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Editorial Board Members’ Collection Series: "AI for Cybersecurity"

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 2024) | Viewed by 5607

Special Issue Editors

Prof. Dr. Xiangjie Kong

E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: urban computing; mobile computing; network science
Special Issues, Collections and Topics in MDPI journals
Dr. Priyadarsi Nanda

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology, Sydney, NSW 2007, Australia
Interests: networking; cybersecurity; IoT; wireless networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ana Rosa Cavalli

E-Mail Website
Guest Editor
Department of Mobile Networks and Services, Institute Mines-Telecom/Telecom Sud Paris, CEDEX, 91011 Evry, France
Interests: networks protocols; networks monitoring; network security; cybersecurity; Internet of Things; formal modelling and testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous occurrence of information theft and poisoning has made society pay more attention to the defense of cybersecurity. The development of AI technology has become a guarantee of cybersecurity to a large extent. Compared with other cyber security defense technologies, AI has very good information processing ability and can deal with unknown problems in a timely manner, e.g., intrusion detection technology can detect malicious and violations of security policy behavior information in a timely manner, while intelligent firewall security technology can enforce access control in intranet and extranet communication through predefined security policies. When users need personalized services, federated learning technology enables data sharing on the basis of ensuring data privacy security and legal compliance. Differential privacy technology ensures that data are not leaked when used for research or analysis. The application of AI has given new connotations to cyberspace.

In order to improve the applications of AI in cybersecurity, new theories, technologies, architectures, algorithms, and mechanisms need to be proposed. This Special Issue aims to gather relevant researchers from industry and academia to share their latest findings and developments in the field of AI for cybersecurity.

We invite high-quality paper submissions of theoretical and experimental natures on topics that include, but are not limited to, the following:

  • Data privacy;
  • Differential privacy;
  • Blockchain;
  • Federated learning;
  • Anomaly detection and analysis;
  • Intelligent firewall technology.

Prof. Dr. Xiangjie Kong
Dr. Priyadarsi Nanda
Prof. Dr. Ana Rosa Cavalli
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

  • artificial intelligence technology
  • cybersecurity
  • personalized service
  • privacy issues
  • data leakage

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Published Papers (2 papers)

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Research

25 pages, 4873 KiB  
Article
Impact of In-Air Gestures on In-Car Task’s Diver Distraction
by Chengyong Cui, Guojiang Shen, Yu Wang, Yile Xu, Hao Du, Wenyi Zhang and Xiangjie Kong
Electronics 2023, 12(7), 1626; https://doi.org/10.3390/electronics12071626 - 30 Mar 2023
Cited by 1 | Viewed by 1589
Abstract
As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the [...] Read more.
As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the impact of in-air gestures on driver distraction during in-vehicle tasks and the scarcity of skeleton-based in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes in-air gestures, classifying them as static or dynamic. Our gesture model, tested on the large-scale AUTSL dataset, demonstrates accuracy comparable to state-of-the-art methods and increased efficiency on mobile devices. In comparative experiments between in-air gestures and touch interactions within a driving simulation environment, we established an evaluation system to assess the driver’s attention level during driving. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS in multi-goal driving environments, significantly improving driving performance by 65%. The proposed framework can serve as a valuable tool for designing future in-air gesture-based interfaces for IVIS, contributing to enhanced cybersecurity. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "AI for Cybersecurity")
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39 pages, 619 KiB  
Article
Toward Building Smart Contract-Based Higher Education Systems Using Zero-Knowledge Ethereum Virtual Machine
by Dénes László Fekete and Attila Kiss
Electronics 2023, 12(3), 664; https://doi.org/10.3390/electronics12030664 - 28 Jan 2023
Cited by 10 | Viewed by 3356
Abstract
The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this [...] Read more.
The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this paper, the most necessary requirements are examined regarding a blockchain-based higher education system, based on the most well-known research papers. Moreover, the opportunities of working on an education system by maintaining a decentralized structure organization are recommended as well. This paper recommends the most suitable blockchain scaling solution for the architecture of an education system which uses the most state-of-the-art EVM (Ethereum virtual machine) compatible approach to implement the higher education system with all the predefined requirements. It is proven that the explained smart contract-based higher education system, which uses zkEVM (zero-knowledge Ethereum virtual machine), consists of all necessary functionalities and satisfies all predefined requirements. In fact, the recommended system, by using a modular blockchain structure, implements all the functionality and capability of the examined related works in one system, namely GDPR (General Data Protection Regulation), which is compatible and more secure. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "AI for Cybersecurity")
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