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Advanced Sensing and Intelligent Technologies for Cybersecurity in the Internet of Things Systems

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

Deadline for manuscript submissions: 10 November 2025 | Viewed by 277

Special Issue Editor


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Guest Editor
Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UK
Interests: cyber security; insider threat; artificial intelligence; networks; cyber-physical systems security; health informatics; multi-criteria decision-making

Special Issue Information

Dear Colleagues,

The rapid expansion of Internet of Things (IoT) technologies has significantly changed modern industries by improving connectivity and automation across various sectors. The IoT has facilitated innovative applications and increased efficiency, but it has also introduced new vulnerabilities and potential points of attack. The growing complexity of these systems, especially when combined with cyber-physical systems and critical infrastructure, has resulted in significant security gaps, making them attractive targets for advanced cyberattacks.

Traditional cybersecurity solutions often struggle to address the specific challenges posed by IoT systems, such as limited resources, scalability issues, and heterogeneity. However, advancements in sensing technologies, edge and fog computing, decentralized architectures, and large language models (LLMs) have enabled new opportunities for IoT security.

This Special Issue aims to encourage research on state-of-the-art sensing and intelligent technologies that enhance the detection, prevention, and mitigation of cybersecurity threats in IoT networks.

We welcome technical contribution papers, industrial case studies, and review articles that focus on innovative strategies for identifying and addressing threats in IoT environments. Topics of interest include, but are not limited to, the following research areas:

  • Advanced sensing techniques for secure IoT;
  • Advanced sensing technologies for intrusion and anomaly detection;
  • Machine learning, AI-driven methods, and large language models (LLMs) for sensor-based anomaly detection in IoT systems;
  • Real-time monitoring and threat detection frameworks;
  • Security architectures based on edge and fog computing;
  • Decentralized trust establishment and reputation systems;
  • Large language models (LLMs) for threat intelligence, vulnerability assessment, and automated responses in IoT and CPS;
  • Multimodal sensing;
  • Privacy-preserving methods for sensor data in IoT networks;
  • Adversarial machine learning in sensor-driven IoT security systems;
  • Blockchain for IoT cyber security;
  • Socio-technical solutions for IoT cyber security.

Dr. Mohammed Al-Mhiqani
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.

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Published Papers (1 paper)

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Research

26 pages, 5549 KiB  
Article
Intrusion Detection and Real-Time Adaptive Security in Medical IoT Using a Cyber-Physical System Design
by Faeiz Alserhani
Sensors 2025, 25(15), 4720; https://doi.org/10.3390/s25154720 (registering DOI) - 31 Jul 2025
Viewed by 47
Abstract
The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical [...] Read more.
The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article
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