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Trustworthy and Secure Internet of Things and Cyber–Physical Systems
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Trustworthy and Secure Internet of Things and Cyber–Physical Systems

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

Deadline for manuscript submissions: 15 January 2027 | Viewed by 2154

Special Issue Editor

Dr. Mohamed Ibrahem

E-Mail Website
Guest Editor
School of Computer and Cyber Sciences, Augusta University, Augusta, GA 30912, USA
Interests: cybersecurity in internet of things (IoT) and cyber–physical systems (CPS); applied cryptography; privacy-preserving artificial intelligence (AI); machine learning for cyber security; traffic analysis attacks and countermeasures; smart healthcare systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid expansion of the Internet of Things (IoT), networks, and cyber–physical systems (CPS) has introduced significant security and privacy challenges. As these interconnected systems have become integral to industries such as smart cities, healthcare, industrial automation, and transportation, they face increasing risks from cyber threats, unauthorized access, and data breaches. This Special Issue intends to present cutting-edge research and innovative solutions addressing security and privacy challenges tailored to IoT, networks, and CPS. Key topics include, but are not limited to, lightweight cryptographic methods, intrusion detection and prevention systems, blockchain applications, secure communication protocols, and trustworthy AI-driven cybersecurity approaches. It will also examine regulatory and ethical considerations to balance security with system performance and compliance to ensure secure and sustainable deployments. By gathering innovative research, this Special Issue aims to advance security frameworks that safeguard IoT, networked systems, and CPS from evolving cyber threats while maintaining efficiency and performance.

Dr. Mohamed Ibrahem
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 and CPS security
  • privacy-preserving techniques
  • network security frameworks
  • lightweight cryptographic protocols
  • intrusion detection and prevention systems
  • blockchain for secure IoT and CPS
  • AI-driven cybersecurity solutions
  • secure communication protocols in IoT and CPS
  • trust management in IoT and CPS
  • resilient architectures for critical infrastructures

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Published Papers (2 papers)

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Research

31 pages, 1182 KB  
Article
Robust Federated-Learning-Based Classifier for Smart Grid Power Quality Disturbances
by Maazen Alsabaan, Abdelrhman Elsayed, Atef Bondok, Mahmoud M. Badr, Mohamed Mahmoud, Tariq Alshawi and Mohamed I. Ibrahem
Sensors 2025, 25(22), 6880; https://doi.org/10.3390/s25226880 - 11 Nov 2025
Viewed by 667
Abstract
The transition from traditional power systems to smart grids demands advanced methods for detecting and classifying Power Quality Disturbances (PQDs)—variations in voltage, current, or frequency that disrupt device performance. The rise of renewable energy and nonlinear loads, such as LED lighting, has increased [...] Read more.
The transition from traditional power systems to smart grids demands advanced methods for detecting and classifying Power Quality Disturbances (PQDs)—variations in voltage, current, or frequency that disrupt device performance. The rise of renewable energy and nonlinear loads, such as LED lighting, has increased PQD occurrences. While deep learning models can effectively analyze data from grid sensors to detect PQD occurrences, privacy concerns often prevent operators from sharing raw data which is necessary to train the models. To address this, Federated Learning (FL) enables collaborative model training without exposing sensitive information. However, FL’s decentralized design introduces new risks, particularly data poisoning attacks, where malicious clients corrupt model updates to degrade the global model accuracy. Despite these risks, PQD classification under FL and its vulnerability to such attacks remain largely unexplored. In this work, we develop FL-based classifiers for PQD detection and compare their performance to traditionally trained, centralized models. As expected from prior FL research, we observed a slight drop in performance: the model’s accuracy decreased from 97% (centralized) to 96% (FL), while the false alarm rate increased from 0.19% to 4%. We also emulate five poisoning scenarios, including indiscriminate attacks aimed at degrading model accuracy and class-specific attacks intended to hide particular disturbance types. Our experimental results show that the attacks are very successful in reducing the accuracy of the classifier. Furthermore, we implement a detection mechanism designed to identify and isolate corrupted client updates, preventing them from influencing the global model. Experimental results reveal that our defense substantially curtails the performance degradation induced by poisoned updates, thereby preserving the robustness of the global model against adversarial influence. Full article
(This article belongs to the Special Issue Trustworthy and Secure Internet of Things and Cyber–Physical Systems)
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40 pages, 3685 KB  
Article
An Explainable Markov Chain–Machine Learning Sequential-Aware Anomaly Detection Framework for Industrial IoT Systems Based on OPC UA
by Youness Ghazi, Mohamed Tabaa, Mohamed Ennaji and Ghita Zaz
Sensors 2025, 25(19), 6122; https://doi.org/10.3390/s25196122 - 3 Oct 2025
Viewed by 1125
Abstract
Stealth attacks targeting industrial control systems (ICS) exploit subtle sequences of malicious actions, making them difficult to detect with conventional methods. The OPC Unified Architecture (OPC UA) protocol—now widely adopted in SCADA/ICS environments—enhances OT–IT integration but simultaneously increases the exposure of critical infrastructures [...] Read more.
Stealth attacks targeting industrial control systems (ICS) exploit subtle sequences of malicious actions, making them difficult to detect with conventional methods. The OPC Unified Architecture (OPC UA) protocol—now widely adopted in SCADA/ICS environments—enhances OT–IT integration but simultaneously increases the exposure of critical infrastructures to sophisticated cyberattacks. Traditional detection approaches, which rely on instantaneous traffic features and static models, neglect the sequential dimension that is essential for uncovering such gradual intrusions. To address this limitation, we propose a hybrid sequential anomaly detection pipeline that combines Markov chain modeling to capture temporal dependencies with machine learning algorithms for anomaly detection. The pipeline is further augmented by explainability through SHapley Additive exPlanations (SHAP) and causal inference using the PC algorithm. Experimental evaluation on an OPC UA dataset simulating Man-In-The-Middle (MITM) and denial-of-service (DoS) attacks demonstrates that incorporating a second-order sequential memory significantly improves detection: F1-score increases by +2.27%, precision by +2.33%, and recall by +3.02%. SHAP analysis identifies the most influential features and transitions, while the causal graph highlights deviations from the system’s normal structure under attack, thereby providing interpretable insights into the root causes of anomalies. Full article
(This article belongs to the Special Issue Trustworthy and Secure Internet of Things and Cyber–Physical Systems)
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