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 791

Special Issue Editor


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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

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Keywords

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

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Published Papers (1 paper)

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Research

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
Viewed by 547
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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