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Special Issue "Security of IoT-Enabled Infrastructures in Smart Cities"

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

Deadline for manuscript submissions: 20 August 2023 | Viewed by 3183

Special Issue Editor

Dr. Wenjuan Li
E-Mail Website
Guest Editor
Department of Computing, Hong Kong Polytechnic University, Hong Kong 100871, China
Interests: network management and security; intrusion detection; spam detection; trust management; web technology; blockchain and E-commerce security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart city aims to optimize various functions with economic development while improving the quality of life for citizens by using smart technologies and data analysis based on information and communication technology (ICT). To achieve this, Internet-of-Things (IoT) is considered as the basis for building a smart city. For example, different sensors will be deployed across the city area to help monitor, analyze, and improve the quality of government service and citizen welfare. However, due to the vulnerabilities of information technology devices, the security of smart city is a big concern. In the IoT environment, cyber-attackers can intrude the whole network by compromising one or more sensors. As a result, there is a great need to protect the security of IoT-enabled smart city infrastructure.

This Special Issue focuses on the security issues within IoT-enabled smart city infrustructure, including threat hunting and defence, and aims to publish research studies and a systematized discussion of the novel ways in developing a more secure and trustworthy smart city.

In particular, the topics of interest include, but are not limited to:

  • Secure IoT architecture for smart city;
  • Trust management for IoT-enabled smart city;
  • Intrusion detection for IoT-enabled smart city;
  • Secure and energy efficient management for IoT-enabled smart city;
  • Threat hunting for for IoT-enabled smart city;
  • Denial-of-service attacks for IoT;
  • Privacy preserving techniques for IoT-enabled smart city;
  • Usable security aspect for for IoT-enabled smart city;
  • Cyber intelligence techniques for IoT-enabled smart city;
  • AI and big data techniques for IoT-enabled smart city.

Dr. Wenjuan Li
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.

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 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.

Published Papers (4 papers)

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Research

Article
Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection
Sensors 2023, 23(3), 1104; https://doi.org/10.3390/s23031104 - 18 Jan 2023
Viewed by 269
Abstract
Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of [...] Read more.
Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms. In this paper, we propose a novel disentangled dynamic deviation transformer network (D3TN) for anomaly detection of multivariate time series, which jointly exploits multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of multivariate time series prediction. Specifically, to disentangle the multiscale graph convolution, we design a novel disentangled multiscale aggregation scheme to better represent the hidden dependencies between sensors to learn fixed inter-sensor dependencies based on static topology. To capture dynamic inter-sensor dependencies determined by real-time monitoring situations and unexpected anomalies, we introduce a self-attention mechanism to model dynamic directed interactions in various potential subspaces influenced by various factors. In addition, complex temporal correlations across multiple time steps are simulated by processing the time series in parallel. Experiments on three real datasets show that the proposed D3TN significantly outperforms the state-of-the-art methods. Full article
(This article belongs to the Special Issue Security of IoT-Enabled Infrastructures in Smart Cities)
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Article
WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City
Sensors 2022, 22(18), 6980; https://doi.org/10.3390/s22186980 - 15 Sep 2022
Cited by 1 | Viewed by 577
Abstract
The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by [...] Read more.
The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%. Full article
(This article belongs to the Special Issue Security of IoT-Enabled Infrastructures in Smart Cities)
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Article
Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology
Sensors 2022, 22(18), 6755; https://doi.org/10.3390/s22186755 - 07 Sep 2022
Viewed by 681
Abstract
The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect [...] Read more.
The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes. Full article
(This article belongs to the Special Issue Security of IoT-Enabled Infrastructures in Smart Cities)
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Article
Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning
Sensors 2022, 22(12), 4522; https://doi.org/10.3390/s22124522 - 15 Jun 2022
Viewed by 1163
Abstract
Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion [...] Read more.
Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities. Full article
(This article belongs to the Special Issue Security of IoT-Enabled Infrastructures in Smart Cities)
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