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AI-Powered Safeguards: Enhancing Security and Privacy in Smart Cities and...
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AI-Powered Safeguards: Enhancing Security and Privacy in Smart Cities and IoT Ecosystems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 2047

Special Issue Editors

Dr. Xin-Wen Wu

E-Mail Website
Guest Editor
Department of Computer Science, University of Mary Washington, Fredericksburg, VA 22401, USA
Interests: theoretical and applied aspects of information and system security; IoT security; applications of AI to cybersecurity; AI security; cybersecurity education
Dr. Waleed Farag

E-Mail Website
Guest Editor
IUP Institute for Cybersecurity, Indiana University of Pennsylvania, Indiana, PA 15705, USA
Interests: autonomous risk detection and mitigation; security of IoT systems; applications of AI and machine learning (NNs, optimization techniques); cybersecurity education and e-learning delivery, assessment, and applications; network security, multimedia data security, and information hiding; multimedia indexing and retrieval techniques/applications

Special Issue Information

Dear Colleagues,

This Special Issue of Electronics aims to explore the transformative role of artificial intelligence (AI) in enhancing security and privacy within smart cities, smart buildings, and smart systems, all of which are increasingly supported by the Internet of Things (IoT). As urban environments and infrastructures become more interconnected, the need for robust security measures and privacy protections becomes paramount. This Issue seeks to gather cutting-edge research and innovative solutions that leverage AI to address these challenges. We invite contributions that delve into AI-driven techniques for threat and anomaly detection, cyber resilience analysis and measurement, risk assessment and response, and all the other security aspects of smart environments. Additionally, we are interested in studies that explore the ethical implications and privacy concerns associated with AI in IoT ecosystems. Topics of interest include, but are not limited to, AI-based cybersecurity frameworks, machine learning algorithms for anomaly detection, automated risk response, AI-powered cyber resilience measurement, privacy-preserving AI models, and the integration of AI with blockchain for enhanced security. By bringing together a diverse range of perspectives and expertise, this Special Issue aims to foster a comprehensive understanding of how AI can be harnessed to create safer, more secure, and privacy-respecting smart environments. We encourage submissions from researchers and practitioners who are at the forefront of this rapidly evolving field.

Dr. Xin-Wen Wu
Dr. Waleed Farag
Guest Editors

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. 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

  • artificial intelligence and machine learning
  • cybersecurity
  • privacy
  • cyber resilience
  • smart systems
  • IoT ecosystems

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Published Papers (2 papers)

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Research

25 pages, 7527 KB  
Article
Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT
by Matthew Boeding, Michael Hempel, Hamid Sharif and Juan Lopez, Jr.
Electronics 2026, 15(3), 692; https://doi.org/10.3390/electronics15030692 - 5 Feb 2026
Viewed by 527
Abstract
The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly [...] Read more.
The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures. Full article
(This article belongs to the Special Issue AI-Powered Safeguards: Enhancing Security and Privacy in Smart Cities and IoT Ecosystems)
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25 pages, 573 KB  
Article
Enhancing IoT Security with Generative AI: Threat Detection and Countermeasure Design
by Alex Oacheșu, Kayode S. Adewole, Andreas Jacobsson and Paul Davidsson
Electronics 2026, 15(1), 92; https://doi.org/10.3390/electronics15010092 - 24 Dec 2025
Viewed by 1046
Abstract
The rapid proliferation of Internet of Things (IoT) devices has increased the attack surface for cyber threats. Traditional intrusion detection systems often struggle to keep pace with novel or evolving threats. This study proposes an end-to-end generative AI-based intrusion detection and response pipeline [...] Read more.
The rapid proliferation of Internet of Things (IoT) devices has increased the attack surface for cyber threats. Traditional intrusion detection systems often struggle to keep pace with novel or evolving threats. This study proposes an end-to-end generative AI-based intrusion detection and response pipeline designed for automated threat mitigation in smart home IoT environments. It leverages a Variational Autoencoder (VAE) trained on benign traffic to flag anomalies, a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model to classify anomalies into five attack categories (C&C, DDoS, Okiru, PortScan, and benign), and Grok3—a large language model—to generate tailored countermeasure recommendations. Using the Aposemat IoT-23 dataset, the VAE model achieves a recall of 0.999 and a precision of 0.961 for anomaly detection. The BERT model achieves an overall accuracy of 99.90% with per-class F1 scores exceeding 0.99. End-to-end prototype simulation involving 10,000 network traffic samples demonstrate a 98% accuracy in identifying cyber attacks and generating countermeasures to mitigate them. The pipeline integrates generative models for improved detection and automated security policy formulation in IoT settings, enhancing detection and enabling quicker and actionable security responses to mitigate cyber threats targeting smart home environments. Full article
(This article belongs to the Special Issue AI-Powered Safeguards: Enhancing Security and Privacy in Smart Cities and IoT Ecosystems)
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