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Applied Sciences
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Advances in Security, Trust and Privacy in Internet of Things
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Advances in Security, Trust and Privacy in Internet of Things

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 6780

Special Issue Editors

Dr. Weidong Fang

E-Mail Website
Guest Editor
Science and Technology on Micro-System Laboratory, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China
Interests: clean energy; smart grid; Internet of Things; cyber security
Special Issues, Collections and Topics in MDPI journals
Dr. Chunsheng Zhu

E-Mail Website
Guest Editor
SUSTech Institute of Future Networks, Southern University of Science and Technology, Shenzhen 518055, China
Interests: Internet of Things; wireless sensor networks; cloud computing; big data; social networks and security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrew W. H. Ip

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
Interests: radio frequency identification (RFID) and supply chain technologies; wireless sensor networks (WSNs); decision support systems and artificial intelligence (AI); big data analytics; resilience engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of Internet of Things (IoT) devices has revolutionized various aspects of modern life, from smart homes and cities to industrial automation and healthcare. However, with this rapid expansion comes a pressing need to address the inherent security, trust, and privacy challenges that accompany the interconnected nature of IoT ecosystems. As IoT devices continue to permeate every facet of society, ensuring robust security measures, establishing trust relationships, and preserving user privacy have become paramount concerns for researchers, practitioners, and policymakers alike.

This Special Issue of Applied Sciences aims to explore recent advancements and emerging trends in addressing the complex challenges associated with securing IoT devices, establishing trust relationships, and safeguarding user privacy in an increasingly interconnected world. By bringing together experts from academia, industry, and government, we seek to foster dialogue and collaboration towards enhancing the security, trustworthiness, and privacy of IoT deployments. By disseminating cutting-edge research and best practices, we hope to empower stakeholders to build more secure, trustworthy, and privacy-respecting IoT systems that enhance the quality of life for individuals and communities worldwide.

We invite submissions addressing a wide range of topics related to security, trust, and privacy in the Internet of Things. Potential areas of interest include, but are not limited to, the following:

  • Network and Cyber Security;
  • Security Policy, Model and Architecture in IoTs;
  • Trust Semantics, Metrics and Models;
  • Trusted Computing Platform;
  • Risk and Reputation Management;
  • Miscellaneous Trust Issues in Cyber Security;
  • Privacy in Mobile and Wireless Communications;
  • Privacy and Anonymity.

Dr. Weidong Fang
Dr. Chunsheng Zhu
Prof. Dr. Andrew W. H. Ip
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

  • Internet of Things
  • cyber security
  • trust
  • privacy preservation

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Published Papers (5 papers)

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Research

25 pages, 907 KiB  
Article
Deterministic Systems for Cryptographic Primitives Used in Security Models in Particular IoT Configurations
by Dana Simian, Oana-Adriana Ticleanu and Nicolae Constantinescu
Appl. Sci. 2025, 15(6), 3048; https://doi.org/10.3390/app15063048 - 11 Mar 2025
Viewed by 558
Abstract
Computing systems grouped in subnets use distributed security models, in general, by creating session keys based on the Diffie–Hellman model, and calculating the necessary parameters for this, on each of the systems. In the particular case of a network of devices heterogeneous in [...] Read more.
Computing systems grouped in subnets use distributed security models, in general, by creating session keys based on the Diffie–Hellman model, and calculating the necessary parameters for this, on each of the systems. In the particular case of a network of devices heterogeneous in terms of computing power, such as IoT, the modeling of a security system of the entire structure will have to take into account the fact that some devices have a very low computing power. In this sense, starting from the study of some general models, used in structures of this type, an integrated structure was developed to secure communications and test certain vulnerable components, to calculate a degree of risk that they are maliciously intended. The system was developed with a customized mathematical model, a scheme for propagation and management of cryptographic parameters and a test in a real environment by creating the algorithmic model and implementing it within a structure of a beneficiary. Full article
(This article belongs to the Special Issue Advances in Security, Trust and Privacy in Internet of Things)
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19 pages, 1377 KiB  
Article
Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control Systems
by Hayriye Tanyıldız, Canan Batur Şahin and Özlem Batur Dinler
Appl. Sci. 2024, 14(20), 9287; https://doi.org/10.3390/app14209287 - 12 Oct 2024
Cited by 1 | Viewed by 1215
Abstract
Industrial control systems (ICSs) are critical components automating the processes and operations of electromechanical systems. These systems are vulnerable to cyberattacks and can be the targets of malicious activities. With increased internet connectivity and integration with the Internet of Things (IoT), ICSs become [...] Read more.
Industrial control systems (ICSs) are critical components automating the processes and operations of electromechanical systems. These systems are vulnerable to cyberattacks and can be the targets of malicious activities. With increased internet connectivity and integration with the Internet of Things (IoT), ICSs become more vulnerable to cyberattacks, which can have serious consequences, such as service interruption, financial losses, and security hazards. Threat actors target these systems with sophisticated attacks that can cause devastating damage. Cybersecurity vulnerabilities in ICSs have recently led to increasing cyberattacks and malware exploits. Hence, this paper proposes to develop a security solution with dynamic and adaptive deceptive patching strategies based on studies on the use of deceptive patches against attackers in industrial control systems. Within the present study’s scope, brief information on the adversarial training method and window size manipulation will be presented. It will emphasize how these methods can be integrated into industrial control systems and how they can increase cybersecurity by combining them with deceptive patch solutions. The discussed techniques represent an approach to improving the network and system security by making it more challenging for attackers to predict their targets and attack methods. The acquired results demonstrate that the suggested hybrid method improves the application of deception to software patching prediction, reflecting enhanced patch security. Full article
(This article belongs to the Special Issue Advances in Security, Trust and Privacy in Internet of Things)
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14 pages, 1096 KiB  
Article
A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
by Chao Wu, Huijuan Hu, Dingju Zhu, Xilin Shan, Kai-Leung Yung and Andrew W. H. Ip
Appl. Sci. 2024, 14(15), 6468; https://doi.org/10.3390/app14156468 - 24 Jul 2024
Viewed by 1284
Abstract
The rapid development of the Internet has facilitated expression, sharing, and interaction on social networks, but some speech may contain harmful discrimination. Therefore, it is crucial to classify such speech. In this paper, we collected discriminatory data from Sina Weibo and propose the [...] Read more.
The rapid development of the Internet has facilitated expression, sharing, and interaction on social networks, but some speech may contain harmful discrimination. Therefore, it is crucial to classify such speech. In this paper, we collected discriminatory data from Sina Weibo and propose the improved Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on Latent Dirichlet Allocation (LDA) to improve data quality and balance. And we propose a new integration method integrating Support Vector Machine (SVM) and Random Forest (RF). The experimental results demonstrate that the integrated model exhibits enhanced precision, recall, and F1 score by 6.0%, 5.4%, and 5.7%, respectively, in comparison with SVM alone. Moreover, it exhibits the best performance in comparison with other machine learning methods. Furthermore, the positive impact of improved SMOTE and this integrated method on model classification is also confirmed in ablation experiments. Full article
(This article belongs to the Special Issue Advances in Security, Trust and Privacy in Internet of Things)
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24 pages, 1024 KiB  
Article
A Reliable Publish–Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
by Behnaz Motamedi and Balázs Villányi
Appl. Sci. 2024, 14(15), 6407; https://doi.org/10.3390/app14156407 - 23 Jul 2024
Viewed by 1692
Abstract
Messaging protocols for the Internet of Things (IoT) play a crucial role in facilitating efficient product creation and waste reduction, and in enhancing agricultural process efficiency within the realm of smart greenhouses. Publish–subscribe (pub-sub) systems improve communication between IoT devices and cloud platforms. [...] Read more.
Messaging protocols for the Internet of Things (IoT) play a crucial role in facilitating efficient product creation and waste reduction, and in enhancing agricultural process efficiency within the realm of smart greenhouses. Publish–subscribe (pub-sub) systems improve communication between IoT devices and cloud platforms. Nevertheless, IoT technology is required to effectively handle a considerable volume of subscriptions or topic adjustments from several clients concurrently. In addition, subscription throughput is an essential factor of the pub-sub mechanism, as it directly influences the speed at which messages may be sent to subscribers. The primary focus of this paper pertains to a performance assessment of the proposed message categorization architecture for the Message Queue Telemetry Transport (MQTT) broker. This architecture aims to establish a standardized approach to pub-sub topics and generate new topics with various performance characteristics. We also standardize the form of MQTT protocol broker topic categorization and payload based on greenhouse specifications. The establishment of topic classification enhances the operational effectiveness of the broker, reduces data volume, and concurrently augments the number of messages and events transmitted from the greenhouse environment to the central server on a per-second basis. Our proposed architecture is validated across multiple MQTT brokers, including Mosquitto, ActiveMQ, Bevywise, and EMQ X, showing enhanced flexibility, extensibility, and simplicity while maintaining full compatibility with greenhouse environments. Key findings demonstrate significant improvements in performance metrics. The message processing time for the proposed Active MQ broker was increased approximately five-fold across all QoS levels compared to the original. Subscription throughput for the Bevywise MQTT Route 2.0 broker at QoS0 reached 1453.053, compared to 290.610 for the original broker. The number of messages in the Active MQ broker at QoS0 surged from 394.79 to 1973.95. These improvements demonstrate the architecture’s potential for broader IoT applications in pub-sub systems. Full article
(This article belongs to the Special Issue Advances in Security, Trust and Privacy in Internet of Things)
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38 pages, 14898 KiB  
Article
Audio Steganalysis Estimation with the Goertzel Algorithm
by Blanca E. Carvajal-Gámez, Miguel A. Castillo-Martínez, Luis A. Castañeda-Briones, Francisco J. Gallegos-Funes and Manuel A. Díaz-Casco
Appl. Sci. 2024, 14(14), 6000; https://doi.org/10.3390/app14146000 - 10 Jul 2024
Cited by 2 | Viewed by 1212
Abstract
Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. [...] Read more.
Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%. Full article
(This article belongs to the Special Issue Advances in Security, Trust and Privacy in Internet of Things)
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