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The Forefront of Internet of Things Cybersecurity with Artificial Intelligence
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The Forefront of Internet of Things Cybersecurity with Artificial Intelligence

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

Deadline for manuscript submissions: 15 September 2026 | Viewed by 15384

Special Issue Editor

Dr. Shingo Yamaguchi

E-Mail Website
Guest Editor
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi, Japan
Interests: IoT; cyber security; AI; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid expansion of the Internet of things (IoT) has significantly increased the complexity and vulnerability of cybersecurity landscapes. In this Special Issue, we aim to explore the critical role of artificial intelligence (AI) in enhancing the security of IoT ecosystems. We invite research that delves into innovative AI-driven approaches to safeguarding connected devices and networks.

We encourage submissions that address the intersection of IoT cybersecurity and AI, focusing on, but not limited to, the following areas:

  • IoT device security.
  • IoT data privacy.
  • AI-driven anomaly detection in IoT systems.
  • AI techniques for IoT security enhancement.
  • AI-powered risk assessment for IoT environments.
  • AI-enabled threat intelligence in the IoT.

We look forward to contributions that provide new insights and advancements in applying AI technologies to the field of IoT security.

Dr. Shingo Yamaguchi
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 250 words) can be sent to the Editorial Office for assessment.

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

  • IoT security
  • IoT data privacy
  • AI-powered cyber security

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Published Papers (4 papers)

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Research

30 pages, 827 KB  
Article
State and Fault Estimation for Uncertain Complex Networks Using Binary Encoding Schemes Under Switching Couplings and Deception Attacks
by Nan Hou, Mengdi Chang, Hongyu Gao, Zhongrui Hu and Xianye Bu
Sensors 2026, 26(1), 182; https://doi.org/10.3390/s26010182 - 26 Dec 2025
Viewed by 389
Abstract
A state and fault estimator is designed in this paper for nonlinear complex networks using binary encoding schemes subject to parameter uncertainties, randomly switching couplings, randomly occurring deception attacks and bounded stochastic noises. A Markov chain is employed to reflect the randomly switching [...] Read more.
A state and fault estimator is designed in this paper for nonlinear complex networks using binary encoding schemes subject to parameter uncertainties, randomly switching couplings, randomly occurring deception attacks and bounded stochastic noises. A Markov chain is employed to reflect the randomly switching phenomena of topological structures (or outer coupling strengths) and internal coupling strengths in complex networks. Binary encoding scheme is utilized to adjust the measurement signal transmission, where the signal is quantized and encoded into a binary bit string which is transmitted via a binary symmetric channel. Random bit flipping resulted from channel noises and randomly occurring deception attacks launched by hacker may take place inevitably during the network transmission process, whose occurrences are represented by two sequences of Bernoulli distributed random variables. The influence of random bit flipping is viewed as an equivalent stochastic noise, which facilitates the estimator design afterwards. The malicious signal is characterized by a nonlinear function satisfying an inequality constraint condition. The received binary bit string is decoded and used for estimating the system state and the fault. This paper aims to design a state and fault estimator such that the estimation error dynamic system is exponentially ultimately bounded in mean square, and the ultimate upper bound is minimized. A sufficient condition is put forth that ensures the existence of the expected state and fault estimator via adopting statistical property analysis, Lyapunov stability theory and matrix inequality technique. An exponentially ultimately bounded state and fault estimator in mean square is designed for such a kind of complex networks using the matrix inequality method. The estimator gain parameter is readily obtained by tackling an optimization issue subject to matrix inequalities constraints using Matlab software. Finally, two simulation examples are carried on which validate the effectiveness of the proposed state and fault estimation approach. The work in this paper plays a role in enriching the research system of estimation for complex network, and providing theoretical guidance for engineering applications. Full article
(This article belongs to the Special Issue The Forefront of Internet of Things Cybersecurity with Artificial Intelligence)
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18 pages, 17129 KB  
Article
Preset-Time Convergence Fuzzy Zeroing Neural Network for Chaotic System Synchronization: FPGA Validation and Secure Communication Applications
by Liang Xiao, Lv Zhao and Jie Jin
Sensors 2025, 25(17), 5394; https://doi.org/10.3390/s25175394 - 1 Sep 2025
Cited by 3 | Viewed by 741
Abstract
Chaotic systems, characterized by extreme sensitivity to initial conditions and complex dynamical behaviors, exhibit significant potential for applications in various fields. Effective control of chaotic system synchronization is particularly crucial in sensor-related applications. This paper proposes a preset-time fuzzy zeroing neural network (PTCFZNN) [...] Read more.
Chaotic systems, characterized by extreme sensitivity to initial conditions and complex dynamical behaviors, exhibit significant potential for applications in various fields. Effective control of chaotic system synchronization is particularly crucial in sensor-related applications. This paper proposes a preset-time fuzzy zeroing neural network (PTCFZNN) model based on Takagi–Sugeno fuzzy control to achieve chaotic synchronization in aperiodic parameter exciting chaotic systems. The designed PTCFZNN model accurately handles the complex dynamic variations inherent in chaotic systems, overcoming the challenges posed by aperiodic parameter excitation to achieve synchronization. Additionally, field-programmable gate array (FPGA) verification experiments successfully implemented the PTCFZNN-based chaotic system synchronization control on hardware platforms, confirming its feasibility for practical engineering applications. Furthermore, experimental studies on chaos-masking communication applications of the PTCFZNN-based chaotic system synchronization further validate its effectiveness in enhancing communication confidentiality and anti-jamming capability, highlighting its important application value for securing sensor data transmission. Full article
(This article belongs to the Special Issue The Forefront of Internet of Things Cybersecurity with Artificial Intelligence)
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20 pages, 1070 KB  
Article
P2ESA: Privacy-Preserving Environmental Sensor-Based Authentication
by Andraž Krašovec, Gianmarco Baldini and Veljko Pejović
Sensors 2025, 25(15), 4842; https://doi.org/10.3390/s25154842 - 6 Aug 2025
Viewed by 825
Abstract
The presence of Internet of Things (IoT) devices in modern working and living environments is growing rapidly. The data collected in such environments enable us to model users’ behaviour and consequently identify and authenticate them. However, these data may contain information about the [...] Read more.
The presence of Internet of Things (IoT) devices in modern working and living environments is growing rapidly. The data collected in such environments enable us to model users’ behaviour and consequently identify and authenticate them. However, these data may contain information about the user’s current activity, emotional state, or other aspects that are not relevant for authentication. In this work, we employ adversarial deep learning techniques to remove privacy-revealing information from the data while keeping the authentication performance levels almost intact. Furthermore, we develop and apply various techniques to offload the computationally weak edge devices that are part of the machine learning pipeline at training and inference time. Our experiments, conducted on two multimodal IoT datasets, show that P2ESA can be efficiently deployed and trained, and with user identification rates of between 75.85% and 93.31% (c.f. 6.67% baseline), can represent a promising support solution for authentication, while simultaneously fully obfuscating sensitive information. Full article
(This article belongs to the Special Issue The Forefront of Internet of Things Cybersecurity with Artificial Intelligence)
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40 pages, 695 KB  
Article
Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and Opportunities
by Yagmur Yigit, Mohamed Amine Ferrag, Mohamed C. Ghanem, Iqbal H. Sarker, Leandros A. Maglaras, Christos Chrysoulas, Naghmeh Moradpoor, Norbert Tihanyi and Helge Janicke
Sensors 2025, 25(6), 1666; https://doi.org/10.3390/s25061666 - 7 Mar 2025
Cited by 22 | Viewed by 12574
Abstract
Critical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and [...] Read more.
Critical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and introduce established benchmarks for evaluating Large Language Models (LLMs) within cybersecurity contexts. Next, we explore core cybersecurity issues, focusing on trust, privacy, resilience, and securability in these vital systems. Building on this foundation, we assess the role of Generative AI and LLMs in enhancing CIP and present insights on applying Agentic AI for proactive defense mechanisms. Finally, we outline future directions to guide the integration of advanced AI methodologies into protecting critical infrastructures. Our paper provides a strategic roadmap for researchers and practitioners committed to fortifying national infrastructures against emerging cyber threats through this synthesis of current challenges, benchmarking strategies, and innovative AI applications. Full article
(This article belongs to the Special Issue The Forefront of Internet of Things Cybersecurity with Artificial Intelligence)
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