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Security and Privacy Challenges in IoT-Driven Smart Environments
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Security and Privacy Challenges in IoT-Driven Smart Environments

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

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 4073

Special Issue Editors

Dr. Tuan Nguyen Gia

E-Mail Website
Guest Editor
Department of Future Technologies, University of Turku, 20500 Turku, Finland
Interests: edge computing; fog computing; edge-AI; autonomous systems; autonomous vehicles; FPGA; swarm of drones; co-robot; energy efficiency and e-health
Special Issues, Collections and Topics in MDPI journals
Dr. Gurjot Singh Gaba

E-Mail Website
Guest Editor
Department of Computer and Information Science (IDA), Linköpings Universitet, Linköping, Sweden
Interests: cyber physical security; Internet of Things; authentication; aviation security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is extensively utilized in various sectors, including smart residences and intelligent urban areas. However, the connection between IoT sensors and cloud servers via wireless technology can lead to security and privacy risks, with the large volume of data sent over networks making the IoT vulnerable to cyberattacks. It is thus essential to protect data, especially sensitive and personal data.

This Special Issue seeks contributions that address the security and privacy issues present in the IoT-driven smart environment. We seek practical and scalable methods that enhance our comprehension of IoT vulnerabilities and provide efficient countermeasures.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Security and privacy concerns in various IoT applications such as smart cities, homes, transportation, healthcare, industry, agriculture, and more.
  • Strong security frameworks, protocols, and methods for IoT systems, such as authentication, authorization, access control, intrusion detection, secure communication, and advanced cryptographic methods.
  • Technologies that protect privacy in IoT systems
  • Security protocols for embedded IoT devices: Covering malware prevention, safeguarding firmware, strengthening the operating system, developing secure software, and ensuring reliable updates.
  • Assessing IoT security measures such as threat analysis, security needs, validation, testing, and ethical hacking.
  • Case studies showing the effective application of security and privacy measures in smart environments based on IoT.

Dr. Nguyen Gia Tuan
Dr. Gurjot Singh Gaba
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. 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.

Keywords

  • security
  • data privacy
  • Internet of Things
  • IoT
  • cyber attack
  • vulnerability
  • smart environment

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Published Papers (2 papers)

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27 pages, 518 KiB  
Article
Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation
by Kayode S. Adewole, Andreas Jacobsson and Paul Davidsson
Sensors 2025, 25(6), 1845; https://doi.org/10.3390/s25061845 - 16 Mar 2025
Viewed by 1252
Abstract
As the proliferation of Internet of Things (IoT) devices grows, challenges in security, privacy, and interoperability become increasingly significant. IoT devices often have resource constraints, such as limited computational power, energy efficiency, bandwidth, and storage, making it difficult to implement advanced security measures. [...] Read more.
As the proliferation of Internet of Things (IoT) devices grows, challenges in security, privacy, and interoperability become increasingly significant. IoT devices often have resource constraints, such as limited computational power, energy efficiency, bandwidth, and storage, making it difficult to implement advanced security measures. Additionally, the diversity of IoT devices creates vulnerabilities and threats that attackers can exploit, including spoofing, routing, man-in-the-middle, and denial-of-service. To address these evolving threats, Intrusion Detection Systems (IDSs) have become a vital solution. IDS actively monitors network traffic, analyzing incoming and outgoing data to detect potential security breaches, ensuring IoT systems remain safeguarded against malicious activity. This study introduces an IDS framework that integrates ensemble learning with rule induction for enhanced model explainability. We study the performance of five ensemble algorithms (Random Forest, AdaBoost, XGBoost, LightGBM, and CatBoost) for developing effective IDS for IoT. The results show that XGBoost outperformed the other ensemble algorithms on two publicly available datasets for intrusion detection. XGBoost achieved 99.91% accuracy and 99.88% AUC-ROC on the CIC-IDS2017 dataset, as well as 98.54% accuracy and 93.06% AUC-ROC on the CICIoT2023 dataset, respectively. We integrate model explainability to provide transparent IDS system using a rule induction method. The experimental results confirm the efficacy of the proposed approach for providing a lightweight, transparent, and trustworthy IDS system that supports security analysts, end-users, and different stakeholders when making decisions regarding intrusion and non-intrusion events. Full article
(This article belongs to the Special Issue Security and Privacy Challenges in IoT-Driven Smart Environments)
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32 pages, 5117 KiB  
Article
Securing the 6G–IoT Environment: A Framework for Enhancing Transparency in Artificial Intelligence Decision-Making Through Explainable Artificial Intelligence
by Navneet Kaur and Lav Gupta
Sensors 2025, 25(3), 854; https://doi.org/10.3390/s25030854 - 30 Jan 2025
Cited by 2 | Viewed by 2159
Abstract
Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) [...] Read more.
Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, and native AI pose threats such as eavesdropping, supply chain vulnerabilities, model poisoning, and adversarial attacks. The increased exposure of sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3GPP, ETSI, OEMs and researchers to embed security and resilience as core components of 6G. While research is advancing, establishing a comprehensive security framework remains a significant challenge. To address these evolving threats, our research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats. By refining model accuracy and ensuring alignment through recursive feature elimination and consistent cross-validation, our approach strengthens the overall security posture of the IoT–6G ecosystem, making it more resilient to adversarial attacks and other vulnerabilities. Full article
(This article belongs to the Special Issue Security and Privacy Challenges in IoT-Driven Smart Environments)
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