Special Issue "Security and Trustworthiness in Industrial IoT"
Deadline for manuscript submissions: 31 August 2022 | Viewed by 5978
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.
Interests: Internet of Things; IIoT; cybersecurity for low-power networks; self-aware networked systems; ICT4D
Special Issues, Collections and Topics in MDPI journals
Interests: adaptive security; cybersecurity; Internet of Things; context-awareness; game theory; WBANs
Special Issues, Collections and Topics in MDPI journals
Industrial IoT (IIoT) systems are revolutionizing industry and businesses by improving service delivery and increasing productivity. They facilitate innovations, developments and disruptive business models in various sectors. However, IIoTs are subject to a multitude of threats. Cyberattacks may have a catastrophic impact on industrial applications including stolen proprietary information and cause physical damage to production systems. The research community has done much work on improving the security of IoT systems, but industrial scenarios bring further constraints to the security solutions. IIoT applications are often safety critical with timing constraints, thus addressing the security and trustworthiness of IIoT systems requires consideration of several dimensions.
This Special Issue encourages authors to submit research results covering security and trust of IIoT systems. Contributions addressing relevant theoretical and practical aspects as well as state-of-the-art review works are welcomed. The Special Issue topics include, but are not limited to:
- IIoT devices and protocols security;
- Tailored security solutions for specific IIoT applications;
- Intrusion detection and prevention system;
- Data security, privacy and trustworthiness;
- Security and trust management for fog and edge computing;
- Machine learning, deep learning and blockchain based security solutions;
- Threat and vulnerability in platforms and protocols;
- Threat models;
- Adaptive security management;
- Security metrics and risks;
- Hardware security.
Dr. Ethiopia Nigussie
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. 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 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.
- industrial IoT
- trust evaluation
- intrusion detection and prevention systems
- hardware security
- adaptive security
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Blockchain-based IoT Edge resource dynamic management techniques that can reliably guarantee IoT Edge resource allocation in distributed AIoT environments
Authors: Yoon-Su Jeong
Affiliation: Department of Information and Communication Convergence Engineering, Daejeon-si 35349, Korea
Abstract: Various research in the AIoT environment have recently been done to process information acquired from IoT devices as big data. However, as the AIoT environment becomes more varied, IoT data processing and verification has become a security issue, and the part of processing IoT data stably is becoming increasingly challenging. Storing IoT data in a dispersed AIoT environment not only makes it difficult to govern data directly, but it also leaves IoT Edge resources vulnerable to natural disasters and other risks (network attacks, manager error handling, service errors, etc.). In this paper, we propose a blockchain-based IoT Edge resource management technique that uses a hierarchical convolutional neural network to assist IoT Edge resource allocation in distributed AIoT environments (CNN). The proposed technique uses the Convolution and Pooling procedures to extract the features of IoT Edge resources layer by layer in order to optimize IoT Edge resource allocation for attack patterns of features. The proposed technique, in particular, successfully validates attack pattern resources by linking each IoT Edge resource with a blockchain-based connection to an IoT Edge resource with an attack pattern with a hash chain. Furthermore, the proposed technique dynamically links IoT Edge resource blocks to reduce resource allocation with attack patterns in dispersed AIoT environments, resulting in fewer errors in asymmetrically hashed IoT resources. According to the results of the performance evaluation, the proposed technique reduced the processing time of IoT Edge resources connected to blockchain by 13.9 % when compared to when they didn't. Furthermore, the verification time of IoT Edge resources was improved by an average of 8.1 % by linking the attribute information of IoT Edge resources to each other according to the probability value of IoT Edge resources, and the verification time of IoT Edge resources was improved by an average of 8.1 %. Furthermore, when IoT Edge resources are linked to blockchain according to grouping size, the suggested technique has resulted in up to 13.5 % improvement in accuracy of IoT Edge resource integrity. When IoT Edge servers validate IoT Edge resources after grouping IoT Edge resources into n, the proposed technique showed an average 10.3 percent decreased integrity verification delay as the blockchain size of IoT Edge resources rose. Finally, the overhead of IoT Edge resources grew by 14.2 % on average as the number of grouped IoT Edge resources increased.