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Trust, Privacy, and Security in IoT Networks
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Trust, Privacy, and Security in IoT Networks

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 5111

Special Issue Editor

Dr. Antonio Muñoz

E-Mail Website
Guest Editor
Department of Computer Science, University of Malaga, 29016 Malaga, Spain
Interests: Internet of Things; monitoring of security properties in clouds; security engineering; secure elements and trusted computing system design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid proliferation of Internet of Things (IoT) technologies has revolutionized various industries, enhancing the way we interact with the digital world. However, the widespread adoption of IoT networks also brings forth unprecedented challenges concerning trust, privacy, and security. This Special Issue aims to address the fundamental issues and novel solutions related to ensuring trustworthiness, safeguarding privacy, and enhancing security in IoT networks.

Scope: This Special Issue, entitled "Trust, Privacy, and Security in IoT Networks", solicits original research articles, reviews, and case studies related but not limited to the following topics:

  • Trust establishment and management in IoT environments.
  • Privacy-preserving techniques for IoT data collection, transmission, and storage.
  • Secure communication protocols for IoT devices and gateways.
  • Identity and access management in IoT ecosystems.
  • Threat modeling, risk assessment, and mitigation strategies for IoT networks.
  • Blockchain and distributed ledger technologies for enhancing IoT security.
  • Intrusion detection and prevention systems for IoT networks.
  • Hardware and software co-design for secure IoT devices.
  • Secure firmware and software updates in resource-constrained IoT devices.
  • Machine learning and AI-based approaches for IoT security.
  • Case studies and real-world implementations showcasing effective security practices.

Dr. Antonio Muñoz
Guest Editor

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 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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21 pages, 2566 KiB  
Article
A Blockchain of Things System for Managing Handcrafted Products in a Cultural Industry
by Youssef Aounzou, Fahd Kalloubi and Abdelhak Boulaalam
Sensors 2024, 24(22), 7384; https://doi.org/10.3390/s24227384 - 20 Nov 2024
Viewed by 586
Abstract
The handicraft sector is often seen as a symbol of a country’s cultural identity, as it relies on specialized traditional techniques, skills, and knowledge that are often passed down through generations. As a result, ensuring the authenticity and integrity of this creative value [...] Read more.
The handicraft sector is often seen as a symbol of a country’s cultural identity, as it relies on specialized traditional techniques, skills, and knowledge that are often passed down through generations. As a result, ensuring the authenticity and integrity of this creative value has become a significant challenge, especially with the growth of counterfeiting techniques in this industry. Thus, integrating digital technologies into such sectors offers numerous operational benefits such as transparency, decentralization, data security, and authenticity needs. This paper presents an innovative approach for the handicraft sector, which exploits blockchain technology and the Internet of Things to guarantee the authenticity of cultural heritage. Through experimental evaluations comparing the decentralized blockchain-based system with traditional centralized methods using key metrics such as response time and transactions per second, this study reveals significant results. The statistical analysis reveals that the decentralized approach improves performance in terms of response times for verification and addition processes compared to the centralized system. Specifically, verification is approximately 4.66 times faster and addition is approximately 4.30 times faster in a decentralized system. However, transaction latency in the decentralized approach is approximately 38.21% higher than in the centralized system. Full article
(This article belongs to the Special Issue Trust, Privacy, and Security in IoT Networks)
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22 pages, 1521 KiB  
Article
Reducing DNS Traffic to Enhance Home IoT Device Privacy
by Marta Moure-Garrido, Carlos Garcia-Rubio and Celeste Campo
Sensors 2024, 24(9), 2690; https://doi.org/10.3390/s24092690 - 24 Apr 2024
Viewed by 1178
Abstract
The deployment of Internet of Things (IoT) devices is widespread in different environments, including homes. Although security is incorporated, homes can become targets for cyberattacks because of their vulnerabilities. IoT devices generate Domain Name Server (DNS) traffic primarily for communication with Internet servers. [...] Read more.
The deployment of Internet of Things (IoT) devices is widespread in different environments, including homes. Although security is incorporated, homes can become targets for cyberattacks because of their vulnerabilities. IoT devices generate Domain Name Server (DNS) traffic primarily for communication with Internet servers. In this paper, we present a detailed analysis of DNS traffic from IoT devices. The queried domains are highly distinctive, enabling attackers to easily identify the IoT device. In addition, we observed an unexpectedly high volume of queries. The analysis reveals that the same domains are repeatedly queried, DNS queries are transmitted in plain text over User Datagram Protocol (UDP) port 53 (Do53), and the excessive generation of traffic poses a security risk by amplifying an attacker’s ability to identify IoT devices and execute more precise, targeted attacks, consequently escalating the potential compromise of the entire IoT ecosystem. We propose a simple measure that can be taken to reduce DNS traffic generated by IoT devices, thus preventing it from being used as a vector to identify the types of devices present in the network. This measure is based on the implementation of the DNS cache in the devices; caching few resources increases privacy considerably. Full article
(This article belongs to the Special Issue Trust, Privacy, and Security in IoT Networks)
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Review

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17 pages, 438 KiB  
Review
Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning
by Ibrahim A. Alwhbi, Cliff C. Zou and Reem N. Alharbi
Sensors 2024, 24(11), 3509; https://doi.org/10.3390/s24113509 - 29 May 2024
Cited by 2 | Viewed by 2589
Abstract
Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, [...] Read more.
Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis. Full article
(This article belongs to the Special Issue Trust, Privacy, and Security in IoT Networks)
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