sensors-logo

Journal Browser

Journal Browser

Data-Driven Cybersecurity and Safety for Critical Applications and Infrastructures

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

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 8923

Special Issue Editors

Department of Informatics Engineering, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: critical infrastructure protection; cyber-physical systems security; desktop management; O&M organization; low-level management support
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, P-3030-290 Coimbra, Portugal
Interests: network and infrastructure management; security; critical infrastructure protection; virtualization of networking and computing resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern critical systems are pervasive and quite diversified in terms of scale, whether related to factories, utility infrastructures, vehicles, or even UAVs. Nevertheless, regardless of their size or scope, such systems have one thing in common: a set of special requirements in terms of security and safety, which ultimately classify them as “critical”. This is due to the sensitive nature of the involved control processes and applications, whose malfunction, whether accidental or resulting from malicious intervention, may pose a significant risk to human lives, assets, or essential services.

With the emergence of complex application scenarios, either new or evolved from existing ones, the distributed nature of such critical systems is quickly unfolding into a massive scale. Smart grids that have pushed infrastructure components such as smart meters and inverters up to the consumer’s doorstep, the creation of services for automated UAV airspace coordination, in the scope of collision avoidance frameworks, and even vehicular networks, whose emergence may help to overcome many of today’s problems associated with sustainable and safe mobility, are just a few examples. Ensuring the reliable, secure, and continuous operation of scenarios with such a scale implies the adoption of data-driven approaches that are capable of dealing with considerable amounts of information to support the dependable (semi)automated analysis and decision mechanisms such applications need to become feasible.

This Special Issue aims to present a collection of studies describing the latest advances in data-driven approaches to security and safety for critical applications, encompassing different application scenarios such as (but not limited to):

  • data-driven cyber-security for critical infrastructures and essential services;
  • vehicular networks and V2X scenarios;
  • 5G-assisted security and safety mechanisms;
  • UAV airspace coordination and navigation;
  • industrial Internet of Things (IoT) safety and security applications;
  • resiliency, stability, and fast control algorithms for smart grids; and
  • algorithms and techniques for data-driven anomaly detection for safety and security.

Dr. Tiago Cruz
Dr. Paulo Simões
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

  • Data-driven security and safety
  • Cyber-security
  • UAV coordination
  • Smart grids
  • V2X applications
  • 5G-assisted security and safety
  • Industrial IoT

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

17 pages, 438 KiB  
Communication
Toward the Integration of Cyber and Physical Security Monitoring Systems for Critical Infrastructures
by Alessandro Fausto, Giovanni Battista Gaggero, Fabio Patrone, Paola Girdinio and Mario Marchese
Sensors 2021, 21(21), 6970; https://doi.org/10.3390/s21216970 - 20 Oct 2021
Cited by 13 | Viewed by 2685
Abstract
Critical Infrastructures (CIs) are sensible targets. They could be physically damaged by natural or human actions, causing service disruptions, economic losses, and, in some extreme cases, harm to people. They, therefore, need a high level of protection against possible unintentional and intentional events. [...] Read more.
Critical Infrastructures (CIs) are sensible targets. They could be physically damaged by natural or human actions, causing service disruptions, economic losses, and, in some extreme cases, harm to people. They, therefore, need a high level of protection against possible unintentional and intentional events. In this paper, we show a logical architecture that exploits information from both physical and cybersecurity systems to improve the overall security in a power plant scenario. We propose a Machine Learning (ML)-based anomaly detection approach to detect possible anomaly events by jointly correlating data related to both the physical and cyber domains. The performance evaluation showed encouraging results—obtained by different ML algorithms—which highlights how our proposed approach is able to detect possible abnormal situations that could not have been detected by using only information from either the physical or cyber domain. Full article
Show Figures

Figure 1

15 pages, 1655 KiB  
Article
Does Free Route Implementation Influence Air Traffic Management System? Case Study in Poland
by Ewa Dudek and Karolina Krzykowska-Piotrowska
Sensors 2021, 21(4), 1422; https://doi.org/10.3390/s21041422 - 18 Feb 2021
Cited by 6 | Viewed by 2229
Abstract
The issue addressed in this publication concerns new Air Traffic Management (ATM) functionality, identified in the Commission Implementing Regulation (EU) No 716/2014, known as Flexible Airspace Management and Free Route (FRA). The authors pose a question—does free route implementation influence air transport safety? [...] Read more.
The issue addressed in this publication concerns new Air Traffic Management (ATM) functionality, identified in the Commission Implementing Regulation (EU) No 716/2014, known as Flexible Airspace Management and Free Route (FRA). The authors pose a question—does free route implementation influence air transport safety? What can be done to maintain the current level of safety and still implement modern solutions? To achieve the aim of this paper a developed concept of Risk Priority Number (RPN) calculation, with determination of main RPN components rating scales, in order to carry out the FMEA (Failure Mode and Effects Analysis) risk analysis of FRA implementation was done. The results allow lining up of the identified potential incompatibilities according to their criticality to the system. In effect it can be said that each modification in a management system, related to safety, influence the safety itself. Nevertheless, this influence does not always lead to negative impact. Full article
Show Figures

Figure 1

Review

Jump to: Research

28 pages, 5829 KiB  
Review
Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review
by Sameh Khalfaoui, Jean Leneutre, Arthur Villard, Ivan Gazeau, Jingxuan Ma and Pascal Urien
Sensors 2021, 21(24), 8415; https://doi.org/10.3390/s21248415 - 16 Dec 2021
Cited by 5 | Viewed by 2666
Abstract
The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature [...] Read more.
The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers. Full article
Show Figures

Figure 1

Back to TopTop