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AI-Enabled Industrial Internet and Its Security, Privacy and Trust Challenge

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6730

Special Issue Editors

Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China
Interests: data fusion; cyberphysical systems security; Industrial Internet
Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
Interests: industrial internet; cyberspace security; AI

Special Issue Information

Dear Colleagues,

The Industrial Internet, which makes machines and devices work together, has been widely used in recent years in many modern industrial fields, such as smart manufacturing, intelligent transportation, and smart healthcare. We can collect plenty of data from the Industrial Internet through machine to machine, machine to gateway, and gateway to cloud connections. These big data can allow us to further use artificial intelligence (AI), e.g., a kind of machine learning approach, to address the problem of how to efficiently utilize the resources of all machines. Unfortunately, however, the Industrial Internet may suffer from potential attacks, such as denial of service (DoS), false-data injection (FDI), and spoof. Therefore, the security for the Industrial Internet is incredibly important for current industrial hardware, software, and algorithms. Any security problems in industrial fields may cause catastrophic effects, such as process control explosion due to some malicious attacks on machines or devices. This Special Issue focuses on recent progress in the Industrial Internet and its security , privacy, and trust challenges.

The topics of interest include but are not limited to:

  • AI-based control for the Industrial Internet;
  • Learning methods assisting with traditional control;
  • New modeling approaches for the Industrial Internet;
  • New conceptions and frameworks supporting the Industrial Internet;
  • Data mining in the Industrial Internet;
  • Machine learning and deep learning for the Industrial Internet;
  • Energy-efficient communication framework for the Industrial Internet;
  • Machine diagnostics and reliability analysis in the Industrial Internet;
  • Security in the Industrial Internet;
  • Industrial process privacy in the Industrial Internet;
  • Data privacy for the Industrial Internet;
  • The trust communication frame for the Industrial Internet.

Dr. Bo Chen
Dr. Zhen Hong
Guest Editors

Manuscript Submission Information

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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. Sensors 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 2600 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

  • sensor signal processing
  • deep learning
  • big data processing and machine learning in sensor systems
  • signal processing and intelligent sensing for Internet-of-Things (IoT) platforms

Published Papers (2 papers)

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Research

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20 pages, 3165 KiB  
Article
FAWPA: A FAW Attack Protection Algorithm Based on the Behavior of Blockchain Miners
by Yang Zhang, Xiaowen Lv, Yourong Chen, Tiaojuan Ren, Changchun Yang and Meng Han
Sensors 2022, 22(13), 5032; https://doi.org/10.3390/s22135032 - 4 Jul 2022
Cited by 4 | Viewed by 1910
Abstract
Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm [...] Read more.
Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner’s credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner’s accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila’s predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners’ credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS. Full article
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Review

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20 pages, 3122 KiB  
Review
The Need for Cybersecurity in Industrial Revolution and Smart Cities
by Antonio Clim, Andrei Toma, Răzvan Daniel Zota and Radu Constantinescu
Sensors 2023, 23(1), 120; https://doi.org/10.3390/s23010120 - 23 Dec 2022
Cited by 6 | Viewed by 4216
Abstract
Cities have grown in development and sophistication throughout human history. Smart cities are the current incarnation of this process, with increased complexity and social importance. This complexity has come to involve significant digital components and has thus come to raise the associated cybersecurity [...] Read more.
Cities have grown in development and sophistication throughout human history. Smart cities are the current incarnation of this process, with increased complexity and social importance. This complexity has come to involve significant digital components and has thus come to raise the associated cybersecurity concerns. Major security relevant events can cascade into the connected systems making up a smart city, causing significant disruption of function and economic damage. The present paper aims to survey the landscape of scientific publication related to cybersecurity-related issues in relation to smart cities. Relevant papers were selected based on the number of citations and the quality of the publishing journal as a proxy indicator for scientific relevance. Cybersecurity will be shown to be reflected in the selected literature as an extremely relevant concern in the operation of smart cities. Generally, cybersecurity is implemented in actual cities through the concerted application of both mature existing technologies and emerging new approaches. Full article
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