Special Issue "Emerging Approaches for Secure and Resilient Cyber-Physical-Social Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2021.

Special Issue Editors

Prof. Dr. Antonio Ficarella
E-Mail Website1 Website2
Guest Editor
Prof. Dr. Antonella Longo
E-Mail Website
Guest Editor
Department of Engineering for Innovation, University of Salento, Via Per Arnesano, I-73100 Lecce, Italy
Interests: data management; cyberphysical social systems; smart cities and citizen science
Dr. Ali A. Ardebili
Guest Editor
Centre for Interdisciplinary Research on Critical Infrastructure Security and Resilience, 73100 Lecce, Italy
Interests: Operation Management; Security and Safety; Socioeconomic Transition; Complex Systems Engineering; Industry 4.0

Special Issue Information

Dear Colleagues,

Security, and particularly cyber-physical-social security, has gained momentum worldwide due to the emergence of high-profile cyberattacks with catastrophic consequences on physical assets and social systems. It is crucial to secure cyber-physical-social systems that are present at the core of society. As smart systems, they include both physical and computational components, which seamlessly integrate and closely interact in order to sense the constantly changing states of the real world. The power grid, the transport network, and information and communication systems are some examples of these systems. They are the society’s “Critical Infrastructures”, which are national assets, and essential to maintaining vital societal functions.

Damage or destruction of critical infrastructures by natural disasters, terrorism, and criminal activity may have negative consequences for the security of a nation and the wellbeing of its citizens. Protecting them is essential, as well as protecting the system of system relationships that a failing system has, as these could render a minor system critical under certain circumstances. These facts unveil the significant importance of increasing the capacity of the critical infrastructures to resist in case of a threat and recover quickly from damages.

Due to the latest advancements, such as sophisticated threat matrices, automation related to the fourth industrial revolution, uncertainty about threat patterns, big data management, the complexity of social functions and structures and the related consequences for privacy and protection of personal data, upcoming attacks and threats have made these critical infrastructures highly insecure. As these infrastructures are heavily interconnected, this, as a result, makes the disruption more sophisticated and targeted. The potential damage caused by human errors in control systems is no less dangerous and could cause the decay of the system.

The scenario is even more complicated by the diverse state of digitalization of critical infrastructures or their ancillary services, and by the presence of diverse stakeholders and actors in their security and safety management. Recent events have demonstrated the increase of combined physical and cyberattacks due to their interdependencies [1]. Disruptions in the operation of a transport asset may result from many kinds of physical attacks and/or cyberattacks on installations and their interconnected systems. In addition, such attacks can easily be triggered with minimum human involvement from remote locations, thus making infrastructures more vulnerable to cyberattacks. Conversely, the involvement of people and ‘soft targets’ could be a fundamental resource in the prevention, detection, reaction, and mitigation phases.

The overall scenario is in continuous evolution, and novel emergent approaches are required to cope with the security and protection from physical and cyber threats and the correlated events.

This Special Issue will collect highly relevant and outstanding research articles that can provide solutions for securing and protecting critical infrastructures. This Special Issue will benefit the research domain and the industry as a result. The Authors are particularly encouraged to submit original articles, literature reviews, and research/discussion notes on critical infrastructures protection, and critical infrastructure cyber-physical-social resilience. Topics of interest include but are not limited to the following:

  • Cyber-physical threats management;
  • Cyber-physical-social systems management;
  • Big data analytics for smart grids security;
  • Security analytics for ICS/SCADA systems;
  • New attack patterns and behavior analysis for critical infrastructure security;
  • Detection and analysis for malware;
  • In-stream data analytics for network monitoring;
  • Big data analysis for critical Infrastructures data storage;
  • CPS data stream visualization and analysis;
  • Collaborative intrusion detection and multiple agents monitoring for critical infrastructure security;
  • Critical-infrastructure-related big data policies and standards;
  • Log analytics for critical infrastructures;
  • Vulnerability analysis for critical infrastructures;
  • Privacy protection for critical infrastructures;
  • Security analysis of intrusion prevention systems for critical infrastructures;
  • Critical infrastructure network forensics;
  • Intrusion detection systems analytics;
  • Security modeling and threat of critical infrastructures;
  • Cybersecurity multistream analysis for critical infrastructure correlations;
  • Security data collection and protection technologies;
  • Trustworthy data process, aggregation, and analytics for critical infrastructure security
  • Applications of cyber-physical-social system approaches in real cases

[1] https://www.csoonline.com/article/3125476/security/the-future-of-security-a-combination-of-cyber-and-physical-defense.html

Prof. Eng. Antonio Ficarella
Dr. Antonella Longo
Dr. Ali A. Ardebili
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 papers will be 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. Applied Sciences 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 2000 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 (2 papers)

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Internet of Things Meet Internet of Threats: New Concern Cyber Security Issues of Critical Cyber Infrastructure
Appl. Sci. 2021, 11(10), 4580; https://doi.org/10.3390/app11104580 - 17 May 2021
Viewed by 391
As a new area of technology, the Internet of Things (IoT) is a flagship and promising paradigm for innovating society. However, IoT-based critical infrastructures are an appealing target for cybercriminals. Such distinctive infrastructures are increasingly sensitive to cyber vulnerabilities and subject to many [...] Read more.
As a new area of technology, the Internet of Things (IoT) is a flagship and promising paradigm for innovating society. However, IoT-based critical infrastructures are an appealing target for cybercriminals. Such distinctive infrastructures are increasingly sensitive to cyber vulnerabilities and subject to many cyberattacks. Thus, protecting these infrastructures is a significant issue for organizations and nations. In this context, raising the cybersecurity posture of critical cyber infrastructures is an extremely urgent international issue. In addition, with the rapid development of adversarial techniques, current cyber threats have become more sophisticated, complicated, advanced and persistent. Thus, given these factors, prior to implementing efficient and resilient cybersecurity countermeasures, identification and in-depth mapping of cyber threats is an important step that is generally overlooked. Therefore, to solve cybersecurity challenges, this study presents a critical analysis of the most recent cybersecurity issues for IoT-based critical infrastructures. We then discuss potential cyber threats and cyber vulnerabilities and the main exploitation strategies adopted by cybercriminals. Further, we provide a taxonomy of cyberattacks that may affect critical cyber infrastructures. Finally, we present security requirements and some realistic recommendations to enhance cybersecurity solutions. Full article
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Cyber Attack Detection Scheme for a Load Frequency Control System Based on Dual-Source Data of Compromised Variables
Appl. Sci. 2021, 11(4), 1584; https://doi.org/10.3390/app11041584 - 10 Feb 2021
Cited by 1 | Viewed by 329
Cyber attacks bring key challenges to the system reliability of load frequency control (LFC) systems. Attackers can compromise the measured data of critical variables of the LFC system, making the data received by the defender unreliable and resulting in system frequency fluctuation or [...] Read more.
Cyber attacks bring key challenges to the system reliability of load frequency control (LFC) systems. Attackers can compromise the measured data of critical variables of the LFC system, making the data received by the defender unreliable and resulting in system frequency fluctuation or even collapse. In this paper, to detect potential attacks on measured data, we propose a novel attack detection scheme using the dual-source data (DSD) of compromised variables. First, we study the characteristics of the compromised LFC system considering potentially vulnerable variables and different types of attack templates. Second, by designing a variable observer, the relationship between the known security variables and the variables which are at risk of being compromised in the LFC system is established. The features of the data obtained by the observer can reflect those of the true data. Third, a Siamese network (SN) is designed to quantify the distance between the characteristics of measured data and that of observed data. Finally, an attack detection scheme is designed by analyzing the similarity of the DSD. Simulation results verify the feasibility of the detection scheme studied in this paper. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.


title: Employ ArƟficial intelligence (AI) to improve resilience of green energy systems


abstract: In our modern world, the urge for green energy is highly spreading in order to satisfy the always increasing energy request and decrease emissions. In our everyday life Artificial Intelligence and its applications have been taking part in every field possible alongside the huge volume of data collected regarding green energy systems; moreover the need of joining cuƫng-edge technologies has become a must not a privilege. The purpose of this study is to review the current scholar literature on the use of AI for improving the resilience of green energy. The energy system is analyzed from a cyber-physical social perspective in order to beter stress the impact on the society. Current study revealed the significant enhancement of resilience applying AI methods and data-driven models (machine learning, deep learning, neural networks, multi-agent, big data, data mining….etc). Moreover, the challenges of implementing AI in green energy systems, and its applications are investigated in order to identify the main features of developing novel approaches to resilience of green energy systems based on AI.

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