Special Issue "Research on Security and Privacy in IoT and Big Data"
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 August 2023 | Viewed by 1259
Special Issue Editors
Interests: wireless network security; applied cryptography; system security & data forensics
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Interests: security and privacy; satellite network security; space IoT security
Interests: big data; cybersecurity; IoT security and privacy
Special Issue Information
Internet of Things (IoT) connects physical or virtual objects to the Internet, covering various domains in our society, from manufacturing to automation, transportation, finance, etc. To make full exploitation of the burgeoning volume of data generated, big data analytics and mining in the IoT context have received considerable attention in both academia and industry. The integrated research on both IoT and big data can have profound effects on building the next-generation intelligent network and services, including smart homes, smart grids, smart traffic, Industrial IoT (IIoT), and intelligent automation. Nevertheless, security and privacy vulnerabilities in IoT and big data arise from diverse aspects, such as the insecure public communication backbone, the IoT hardware, and software attack surface, as well as the privacy threats incurred by big data analysis. Therefore, it is important to explore and investigate the security and privacy issues that exist related to IoT and big data.
This Special Issue mainly focuses on putting together original research and review works emerging in the IoT and big data domain, aiming at presenting the recent advanced technologies, solutions, and approaches on solving the privacy and security challenges in this field.
Potential topics include, but are not limited to:
- Architectures and frameworks for securing IoT;
- AI-based data analytics for securing IoT against attacks;
- Privacy and security in IIoT and Industry 4.0;
- Edge computing for IoT security and privacy;
- Blockchains for securing IoT systems;
- Security and privacy for digital twin technology;
- Data-centric security and privacy in mobile computing;
- Federated learning for securing IoT systems;
- Information/operational technology security in IoT;
- Advanced cryptography schemes for big data in IoT;
- Trust-based solutions for big data in IoT;
- Intrusion detection and prevention for IoT big data;
- Security and privacy in smart city;
- Risk assessment and control in IoT systems;
- Security and privacy in satellite IoT applications.
Dr. Rongxing Lu
Dr. Qinglei Kong
Dr. Xichen Zhang
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. Applied Sciences 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 2300 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.
- big data
- privacy and security
- machine learning