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Security and Privacy Technologies in Smart Cities: Advances in Anomaly Detection and Privacy-Preserving Solutions

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

Deadline for manuscript submissions: 25 October 2025 | Viewed by 860

Special Issue Editors

Dr. Mohamed Baza

E-Mail Website
Guest Editor
Department of Computer Science, College of Charleston, Charleston, SC, USA
Interests: security; blockchain; smart grids; FL; adversarial machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Tara Salman

E-Mail Website
Guest Editor
Department of Computer Science, Texas Tech University, Lubbock, TX, USA
Interests: cybersecurity; security in sensor-based healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities leverage cutting-edge technologies like the Internet of Things (IoT), big data analytics, and intelligent infrastructures to offer improved services and enhanced quality of life. However, the increasing integration of these technologies raises significant concerns about privacy and security. This Special Issue aims to gather innovative research focused on enhancing the security and privacy of smart city applications. Topics of interest include advanced anomaly detection systems, secure sensor networks, and privacy-preserving techniques tailored to smart cities. Additionally, it explores interdisciplinary solutions involving blockchain, cryptography, and adversarial machine learning to address emerging challenges in securing critical smart city infrastructures, such as intelligent transportation, healthcare systems, and smart grids. The objective is to provide a platform for researchers to present novel solutions that enhance the resilience of smart city applications against cyber threats while also safeguarding user privacy.

Dr. Mohamed Baza
Dr. Tara Salman
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • smart city security
  • privacy-preserving technologies
  • anomaly detection
  • blockchain and cryptography
  • IoT security
  • adversarial machine learning
  • secure sensor networks
  • federated learning
  • healthcare security in smart cities

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Published Papers (1 paper)

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Research

21 pages, 666 KiB  
Article
Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning
by Alyaman H. Massarani, Mahmoud M. Badr, Mohamed Baza, Hani Alshahrani and Ali Alshehri
Sensors 2025, 25(13), 4111; https://doi.org/10.3390/s25134111 - 1 Jul 2025
Viewed by 514
Abstract
Electricity theft remains a pressing challenge in modern smart grid systems, leading to significant economic losses and compromised grid stability. This paper presents a sensor-driven framework for electricity theft detection that leverages data collected from smart meter sensors, key components in smart grid [...] Read more.
Electricity theft remains a pressing challenge in modern smart grid systems, leading to significant economic losses and compromised grid stability. This paper presents a sensor-driven framework for electricity theft detection that leverages data collected from smart meter sensors, key components in smart grid monitoring infrastructure. The proposed approach combines prototype learning and meta-level ensemble learning to develop a scalable and accurate detection model, capable of identifying zero-day attacks that are not present in the training data. Smart meter data is compressed using Principal Component Analysis (PCA) and K-means clustering to extract representative consumption patterns, i.e., prototypes, achieving a 92% reduction in dataset size while preserving critical anomaly-relevant features. These prototypes are then used to train base-level one-class classifiers, specifically the One-Class Support Vector Machine (OCSVM) and the Gaussian Mixture Model (GMM). The outputs of these classifiers are normalized and fused in a meta-OCSVM layer, which learns decision boundaries in the transformed score space. Experimental results using the Irish CER Smart Metering Project (SMP) dataset show that the proposed sensor-based detection framework achieves superior performance, with an accuracy of 88.45% and a false alarm rate of just 13.85%, while reducing training time by over 75%. By efficiently processing high-frequency smart meter sensor data, this model contributes to developing real-time and energy-efficient anomaly detection systems in smart grid environments. Full article
(This article belongs to the Special Issue Security and Privacy Technologies in Smart Cities: Advances in Anomaly Detection and Privacy-Preserving Solutions)
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