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IoT Security in the Age of AI: Innovative Approaches and...
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IoT Security in the Age of AI: Innovative Approaches and Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 2892

Special Issue Editor

Dr. Yazan Otoum

E-Mail Website
Guest Editor
1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
2. School of Computer Science and Technology, Algoma University, ON P6A 2G4, Canada
Interests: artificial intelligence; IoT—Internet of Things; cybersecurity; e-Health; smart cities

Special Issue Information

Dear Colleagues,

The rapid expansion of the Internet of Things (IoT) continues to revolutionize various sectors by enabling smart devices to communicate and collaborate seamlessly. However, this growth also introduces significant security challenges that must be addressed to protect sensitive data, maintain user privacy, and ensure the integrity of IoT networks. The need to secure IoT systems against an increasingly sophisticated landscape of cyber threats has become paramount, especially as IoT applications penetrate critical domains such as healthcare, smart cities, industrial automation, and transportation.

In this Special Issue, we are particularly interested in papers that explore innovative approaches, frameworks, and technologies to enhance the security of IoT systems. We invite submissions that address new methods for detecting and mitigating security vulnerabilities, protecting IoT devices against attacks, and developing secure communication protocols for IoT networks.

Topics of interest include, but are not limited to:

  • Advanced cryptographic techniques and lightweight security solutions for IoT devices;
  • Machine learning and artificial intelligence approaches for IoT threat detection and response;
  • Secure communication protocols and architectures for IoT networks;
  • Privacy-enhancing technologies and data protection methods for IoT applications;
  • Blockchain and distributed ledger technologies for IoT security;
  • Federated learning approaches to secure IoT systems and protect user data;
  • Utilization of large language models (LLMs) for anomaly detection and predictive security in IoT environments;
  • Risk assessment, threat modeling, and security frameworks for IoT ecosystems;
  • Case studies and real-world applications demonstrating IoT security implementations;
  • Security considerations in IoT edge computing and fog computing environments.

We encourage submissions that provide insights into emerging security challenges, present new technological solutions, or propose innovative models for enhancing the security posture of IoT systems.

Dr. Yazan Otoum
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. Electronics 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

  • IoT security
  • federated learning
  • large language models (LLMs)
  • machine learning for IoT
  • cybersecurity
  • blockchain technology

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Published Papers (4 papers)

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Research

19 pages, 904 KiB  
Article
Enhancing Subband Speech Processing: Integrating Multi-View Attention Module into Inter-SubNet for Superior Speech Enhancement
by Jeih-Weih Hung, Tsung-Jung Li and Bo-Yu Su
Electronics 2025, 14(8), 1640; https://doi.org/10.3390/electronics14081640 - 18 Apr 2025
Viewed by 245
Abstract
The Inter-SubNet speechenhancement network improves subband interaction by enabling the exchange of complementary information across frequency bands, ensuring robust feature refinement while significantly reducing computational load through lightweight, subband-specific modules. Despite its compact design, it outperforms state-of-the-art models such as FullSubNet, FullSubNet+, Conv-TasNet, [...] Read more.
The Inter-SubNet speechenhancement network improves subband interaction by enabling the exchange of complementary information across frequency bands, ensuring robust feature refinement while significantly reducing computational load through lightweight, subband-specific modules. Despite its compact design, it outperforms state-of-the-art models such as FullSubNet, FullSubNet+, Conv-TasNet, and DCCRN+, offering a highly efficient yet powerful solution. To further enhance its performance, we propose integrating a Multi-view Attention (MA) module as a front-end or intermediate component. The MA module utilizes attention mechanisms across channel, global, and local views to emphasize critical features, ensuring comprehensive speech signal processing. Evaluations on the VoiceBank-DEMAND dataset show that incorporating the MA module significantly improves metrics like SI-SNR, PESQ, and STOI, demonstrating its effectiveness in enhancing subband feature extraction and overall speech enhancement performance. Full article
(This article belongs to the Special Issue IoT Security in the Age of AI: Innovative Approaches and Technologies)
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14 pages, 2232 KiB  
Article
Secure and Lightweight Firmware Over-the-Air Update Mechanism for Internet of Things
by Chae-Yeon Park, Sun-Jin Lee and Il-Gu Lee
Electronics 2025, 14(8), 1583; https://doi.org/10.3390/electronics14081583 - 14 Apr 2025
Viewed by 239
Abstract
The Internet of Things (IoT) necessitates secure and lightweight firmware over-the-air (FOTA) update mechanisms for remote device management and timely mitigation of security vulnerabilities. This study introduces an FOTA update method to mitigate man-in-the-middle attacks in resource-constrained environments. The proposed method minimizes firmware [...] Read more.
The Internet of Things (IoT) necessitates secure and lightweight firmware over-the-air (FOTA) update mechanisms for remote device management and timely mitigation of security vulnerabilities. This study introduces an FOTA update method to mitigate man-in-the-middle attacks in resource-constrained environments. The proposed method minimizes firmware file size and encryption overhead through a dual-XOR operation and DEFLATE compression, while enhancing security via multiple transmission channels. It improves performance in terms of latency, memory usage, and power consumption but also maintains security against brute-force attacks during MITM attacks. Full article
(This article belongs to the Special Issue IoT Security in the Age of AI: Innovative Approaches and Technologies)
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34 pages, 14102 KiB  
Article
Adversarial Attacks on Supervised Energy-Based Anomaly Detection in Clean Water Systems
by Naghmeh Moradpoor, Ezra Abah, Andres Robles-Durazno and Leandros Maglaras
Electronics 2025, 14(3), 639; https://doi.org/10.3390/electronics14030639 - 6 Feb 2025
Viewed by 846
Abstract
Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity. The protection of these infrastructures is crucial, as nations depend on their operation and stability. However, cyberattacks on such [...] Read more.
Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity. The protection of these infrastructures is crucial, as nations depend on their operation and stability. However, cyberattacks on such systems appear to be increasing in both frequency and severity. Various machine learning approaches have been employed for anomaly detection in Critical National Infrastructure, given their success in identifying both known and unknown attacks with high accuracy. Nevertheless, these systems are vulnerable to adversarial attacks. Hackers can manipulate the system and deceive the models, causing them to misclassify malicious events as benign, and vice versa. This paper evaluates the robustness of traditional machine learning techniques, such as Support Vector Machines (SVMs) and Logistic Regression (LR), as well as Artificial Neural Network (ANN) algorithms against adversarial attacks, using a novel dataset captured from a model of a clean water treatment system. Our methodology includes four attack categories: random label flipping, targeted label flipping, the Fast Gradient Sign Method (FGSM), and Jacobian-based Saliency Map Attack (JSMA). Our results show that, while some machine learning algorithms are more robust to adversarial attacks than others, a hacker can manipulate the dataset using these attack categories to disturb the machine learning-based anomaly detection system, allowing the attack to evade detection. Full article
(This article belongs to the Special Issue IoT Security in the Age of AI: Innovative Approaches and Technologies)
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13 pages, 8080 KiB  
Article
Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security
by Kai Gao, Ji-Hwei Horng, Ching-Chun Chang and Chin-Chen Chang
Electronics 2024, 13(21), 4216; https://doi.org/10.3390/electronics13214216 - 27 Oct 2024
Cited by 1 | Viewed by 950
Abstract
The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to [...] Read more.
The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction while also enhancing security through a secret sharing mechanism. Experimental results demonstrate that the proposed scheme achieves approximately 50% accuracy in detecting steganographic text across two steganalysis networks. Additionally, the generated steganographic text preserves the semantic information of the cover text, evidenced by a BERT score of 0.948. This indicates that the proposed scheme performs well in terms of security. Full article
(This article belongs to the Special Issue IoT Security in the Age of AI: Innovative Approaches and Technologies)
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