Security of IoT-Enabled Infrastructures in Smart Cities and Critical Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 9889

Special Issue Editors


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Guest Editor
Computer Sciences Department, Université Libre de Bruxelles, 1070 Brussels, Belgium
Interests: computer science; computer security; cybersecurity; cryptography
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Cybersecurity Research Center, Ecole Polytechnique de Bruxelles, Université Libre de Bruxelles, 50 Avenue Franklin Roosevelt CP 165/56, 1050 Brussels, Belgium
Interests: cybersecurity; network security; IoT; wireless network; 5G
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in wireless communication and hardware technologies have revolutionized the way that humans, computers, and electronic devices interact and exchange information. The advent of the Internet of Things together with the availability of low-delay, reliable, and pervasive communications are transforming our society. New opportunities are emerging in the domain of intelligent transportation systems, smart home, power grid, healthcare systems, robotics, etc. This allows for the optimization of city operation efficiency, communication, and public services offered to citizens, paving the way to smart cities.

Smart cities and connected critical systems must be adequately protected; therefore, questions are being raised about the security of the IoT-based architectures, the privacy of pervasive data collection, and the resilience of critical cyber–physical systems. Emerging solutions will take advantage of recent techniques, such as hardware–software secure co-design, secure data manipulation, and software-defined internet of things and communication.

This Special Issue will promote state-of-the-art research covering all aspects of security, privacy, and trust in IoT-enabled infrastructures, critical systems, and therefore in Smart Cities. High quality contributions addressing related theoretical and practical aspects are expected.

The topics of interest include but are not limited to the following:

  • Innovative techniques for IoT infrastructure security
  • Foundations for a common security framework in IoT-enabled solutions
  • Security and privacy frameworks for IoT-based smart cities
  • Critical infrastructure resilience and security in smart cities
  • Privacy-enabled and secure data manipulation in IoT
  • Secure interoperability architectures for heterogeneous IoT and mixed IoT/5G systems
  • Hardware and software co-design for secure IoT
  • Physical-layer security in wireless IoT

Prof. Olivier Markowitch
Prof. Jean-Michel Dricot
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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
  • smart cities
  • critical systems
  • secure communication
  • secure computation
  • privacy

Published Papers (3 papers)

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Research

23 pages, 1343 KiB  
Article
A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks
by Konstantinos Ntafloukas, Liliana Pasquale, Beatriz Martinez-Pastor and Daniel P. McCrum
Future Internet 2023, 15(3), 100; https://doi.org/10.3390/fi15030100 - 28 Feb 2023
Viewed by 2088
Abstract
Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The [...] Read more.
Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system. Full article
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22 pages, 805 KiB  
Article
Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service
by Ning Chen and Yu Chen
Future Internet 2022, 14(2), 54; https://doi.org/10.3390/fi14020054 - 10 Feb 2022
Cited by 7 | Viewed by 2635
Abstract
The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although [...] Read more.
The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although there is fast development of Smart Cities and the growth of Internet of Things (IoT), real-time anomalous behavior detection in Intelligent Transportation Systems (ITS) is still challenging. Because of multiple advanced features including flexibility, safety, and ease of manipulation, quadcopter drones have been widely adopted in many areas, from service improvement to urban surveillance, and data collection for scientific research. In this paper, a Smart Urban traffic Monitoring (SurMon) scheme is proposed employing drones following an edge computing paradigm. A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision-making at the edge. Specifically, we propose to identify anomalous vehicle behaviors in real time by creatively applying the multidimensional Singular Spectrum Analysis (mSSA) technique in space to detect the different vehicle behaviors on roads. Multiple features of vehicle behaviors are fed into channels of the mSSA procedure. Instead of trying to create and define a database of normal activity patterns of vehicles on the road, the anomaly detection is reformatted as an outlier identifying problem. Then, a cascaded Capsules Network is designed to predict whether the behavior is a violation. An extensive experimental study has been conducted and the results have validated the feasibility and effectiveness of the SurMon scheme. Full article
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21 pages, 2914 KiB  
Article
Evaluation of a Reputation Management Technique for Autonomous Vehicles
by Darius Kianersi, Suraj Uppalapati, Anirudh Bansal and Jeremy Straub
Future Internet 2022, 14(2), 31; https://doi.org/10.3390/fi14020031 - 19 Jan 2022
Cited by 10 | Viewed by 4020
Abstract
Future autonomous vehicles will rely heavily on sharing and communicating information with other vehicles to maximize their efficiency. These interactions, which will likely include details about the positions of surrounding vehicles and obstacles on the road, are essential to their decision-making and the [...] Read more.
Future autonomous vehicles will rely heavily on sharing and communicating information with other vehicles to maximize their efficiency. These interactions, which will likely include details about the positions of surrounding vehicles and obstacles on the road, are essential to their decision-making and the prevention of accidents. However, malicious vehicles—those that intentionally communicate false information—have the capacity to adversely influence other vehicles in the network. This paper presents and evaluates a reputation management system, capable of identifying malicious actors, to mitigate their effects on the vehicle network. The viability of multiple report weighting schemes to calculate reputation is evaluated through a simulation, and a blockchain-based backend for the reputation management system to securely maintain and communicate reputation data is proposed. Storage and computational challenges are considered. This paper shows that weighting schemas, related to the number and reputation of witnesses, positively affect the accuracy of the model and are able to identify malicious vehicles in a network with consistent accuracy and scalability. Full article
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