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Applied Sciences
Special Issues
Research on Security and Privacy in IoT and Big Data
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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: closed (30 August 2023) | Viewed by 6116

Special Issue Editors

Dr. Rongxing Lu

E-Mail Website
Guest Editor
Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Interests: wireless network security; applied cryptography; system security & data forensics
Special Issues, Collections and Topics in MDPI journals
Dr. Qinglei Kong

E-Mail Website
Guest Editor
Institute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen 518055, China
Interests: security and privacy; satellite network security; space IoT security
Dr. Xichen Zhang

E-Mail Website
Guest Editor
Canadian Institute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Interests: big data; cybersecurity; IoT security and privacy

Special Issue Information

Dear Colleagues,

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
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. 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 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

  • big data
  • IoT
  • privacy and security
  • machine learning

Published Papers (3 papers)

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Research

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21 pages, 1999 KiB  
Article
Cluster-Based Approaches toward Developing a Customer Loyalty Program in a Private Security Company
by Arthur de Sousa, Sérgio Moro and Renato Pereira
Appl. Sci. 2024, 14(1), 78; https://doi.org/10.3390/app14010078 - 21 Dec 2023
Viewed by 639
Abstract
This study aimed to create a loyalty program for a private security company’s most valuable customers using clustering techniques on a dataset from the company. K-means was employed as an unsupervised machine learning algorithm to segment customers. Performance evaluation metrics, including the silhouette [...] Read more.
This study aimed to create a loyalty program for a private security company’s most valuable customers using clustering techniques on a dataset from the company. K-means was employed as an unsupervised machine learning algorithm to segment customers. Performance evaluation metrics, including the silhouette coefficient, were utilized to compare various algorithmic approaches. As a distinctive feature of this study, in addition to the evaluation metric, strategic questionnaires were administered to business decision-makers to facilitate the integrated development of a loyalty program with key stakeholders invested in customer retention and profitability. The results show the existence of three customer clusters with an optimal silhouette coefficient for loyalty program development. Interestingly, the customer group to be targeted for the loyalty program did not exhibit the highest silhouette coefficient metric. Business leaders selected the group they perceived as most efficient for program implementation. Consequently, the study concludes that customer segmentation not only entails statistical analyses of individual user groups but also requires a comprehensive understanding of the business and collaboration with stakeholders. Furthermore, this study aligns with findings from other authors, demonstrating that private security companies can benefit from implementing a loyalty program, although avenues for further investigation remain. Full article
(This article belongs to the Special Issue Research on Security and Privacy in IoT and Big Data)
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19 pages, 995 KiB  
Article
A Robust and Effective Two-Factor Authentication (2FA) Protocol Based on ECC for Mobile Computing
by Kaijun Liu, Zhou Zhou, Qiang Cao, Guosheng Xu, Chenyu Wang, Yuan Gao, Weikai Zeng and Guoai Xu
Appl. Sci. 2023, 13(7), 4425; https://doi.org/10.3390/app13074425 - 30 Mar 2023
Cited by 1 | Viewed by 2073
Abstract
The rapid development of mobile computing (e.g., mobile health, mobile payments, and smart homes) has brought great convenience to our lives. It is well-known that the security and privacy of user information from these applications and services is critical. Without the prevention provided [...] Read more.
The rapid development of mobile computing (e.g., mobile health, mobile payments, and smart homes) has brought great convenience to our lives. It is well-known that the security and privacy of user information from these applications and services is critical. Without the prevention provided by an authentication mechanism, safety vulnerabilities may accumulate, such as illegal intrusion access resulting in data leakage and fraudulent abuse. Luckily, the two-factor authentication (2FA) protocols can secure access and communication for mobile computing. As we understand it, existing 2FA authentication protocols weaken security in the pursuit of high efficiency. How efficiency can be achieved while preserving the protocol’s security remains a challenge. In this study, we designed a robust and effective 2FA protocol based on elliptic curve cryptography (ECC) for authentication of users and service providers. We proved the robustness (respectively, the effectiveness) of the presented protocol with the heuristic analysis and security verification provided by the ProVerif tool (respectively, with a performance comparison based on six schemes). Performance comparisons in terms of message rounds, communication, and computation overheads showed that our scheme was superior to the exiting schemes or comparable as a whole; i.e., only two rounds, 1376 bits, and 1.818 ms were required in our scheme, respectively. The evaluation results showed that the proposed 2FA protocol provides a better balance between security and availability compared to state-of-the-art protocols. Full article
(This article belongs to the Special Issue Research on Security and Privacy in IoT and Big Data)
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Review

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27 pages, 412 KiB  
Review
Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
by Euclides Carlos Pinto Neto, Somayeh Sadeghi, Xichen Zhang and Sajjad Dadkhah
Appl. Sci. 2023, 13(11), 6497; https://doi.org/10.3390/app13116497 - 26 May 2023
Cited by 3 | Viewed by 2768
Abstract
The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have been several successful applications of IoT in the industry. For example, in transportation systems, the novel internet of vehicles (IoV) concept has enabled new [...] Read more.
The internet of things (IoT) represents a disruptive concept that has been changing society in several ways. There have been several successful applications of IoT in the industry. For example, in transportation systems, the novel internet of vehicles (IoV) concept has enabled new research directions and automation solutions. Moreover, reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated remarkable success in solving complex problems in different applications. In recent years, new solutions have been developed based on this combined framework (i.e., federated reinforcement learning). Conversely, there is a lack of analysis concerning IoT applications and a standard view of challenges and future directions of the current FRL landscape. Thereupon, the main goal of this research is to present a literature review of federated reinforcement learning (FRL) applications in IoT from multiple perspectives. We focus on analyzing applications in multiple areas (e.g., security, sustainability and efficiency, vehicular solutions, and industrial services) to highlight existing solutions, their characteristics, and research gaps. Additionally, we identify key short- and long-term challenges leading to new opportunities in the field. This research intends to picture the current FRL ecosystem in IoT to foster the development of new solutions based on existing challenges. Full article
(This article belongs to the Special Issue Research on Security and Privacy in IoT and Big Data)
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