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Security and Trust in Next Generation Cyber-Physical Systems

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 30994

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Special Issue Editors

Dr. Emanuele Bellini

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Department of Mathematics and Physics, Università della Campania “Luigi Vanvitelli”, viale Lincoln, 81100 Caserta, Italy
Interests: distributed ledger technologies; cyber-physical systems security and resilience; critical infrastructure protection; human–machine interaction
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Dr. Fiammetta Marulli

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Department of Mathematics and Physics, Università della Campania “Luigi Vanvitelli”, Viale Lincoln, 81100 Caserta, Italy
Interests: artificial intelligence; machine and deep learning; federated deep learning on cloud systems; data analytics and data science applied to Internet of Things and cyber-physical systems; natural language processing
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Dr. Stefano Marrone

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Dipartimento di Matematica e Fisica, Università degli Studi della Campania “Luigi Vanvitelli”, 81100 Caserta, Italy
Interests: model-driven engineering; software testing; critical system design and assurance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The pervasiveness of cyber-physical systems (CPSs) in the control and supervision of a large number of safety/business-critical applications requires security levels capable of dealing with threats whose frequency and effectiveness are increasing. Due to the computational, communication, and power-related limitations of the devices on which CPSs rely, traditional design and verification methodologies and techniques used in ICT must be improved and adapted to this new context. Examples of CPS include smart grid, autonomous transportation systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics.

As commercial-off-the-shelf devices are now part of the control and supervision of such systems, often supporting legacy protocols not originally designed for an open and un-secure network, designing and ensuring security and trust in such systems is of paramount importance. Cybersecurity threats exploit the increased complexity and connectivity of critical infrastructure systems, placing the nation’s security, economy, public safety, and health at risk.

This Special Issue is devoted to collecting novel and original approaches that ensure the security of current and emerging cyber-physical systems by taking into consideration the unique challenges present in this environment. This Special Issue also aims to foster a research community that is strongly committed to technology transfer. Industrial experiences, showing the application of cutting-edge techniques to real-world case studies, are also welcome.

Topics include but are not limited to the following:

Formal methods for security and trust;
Security-by-design approaches;
Vulnerability evaluation of CPSs;
Reasoning and machine learning for fraud detection;
Distributed ledger technology for reputation and trust management in CPSs;
Cyber resilience foundations and emerging approaches in CPSs;
Gamification in cyber security;
Post-quantum security;
Deep and reinforcement learning for adversarial approaches;
Artificial intelligence, blockchain and federated learning technologies for data and model security.

Dr. Emanuele Bellini
Dr. Fiammetta Marulli
Dr. Stefano Marrone
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. Electronics 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.

Keywords

formal modeling and analysis
cyber-physical systems
Internet of Things
distributed ledger technologies
machine learning
artificial-intelligence-based approaches for cyber security and cyber-physical systems
deep neural networks adversarial approaches for vulnerability and threat assessment

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Published Papers (9 papers)

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Research

11 pages, 1343 KiB

Open AccessArticle

Construction and Analysis of Integral User-Oriented Trustworthiness Metrics

by Evgenia Novikova, Elena Doynikova, Diana Gaifulina and Igor Kotenko

Electronics 2022, 11(2), 234; https://doi.org/10.3390/electronics11020234 - 12 Jan 2022

Cited by 1 | Viewed by 1539

Abstract

Trustworthiness metrics help users to understand information system’s or a device’s security, safety, privacy, resilience, and reliability level. These metrics have different types and natures. The challenge consists of the integration of these metrics into one clear, scalable, sensitive, and reasonable metric representing overall trustworthiness level, useful for understanding if the users can trust the system or for the comparison of the devices and information systems. In this research, the authors propose a novel algorithm for calculation of an integral trustworthiness risk score that is scalable to any number of metrics, considers their criticality, and does not perform averaging in a case when all metrics are of equal importance. The obtained trustworthiness risk score could be further transformed to trustworthiness level. The authors analyze the resulting integral metric sensitivity and demonstrate its advantages on the series of experiments. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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16 pages, 386 KiB

Open AccessArticle

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

by Federica Vitale, Bruno Carbonaro, Gennaro Cordasco, Anna Esposito, Stefano Marrone, Gennaro Raimo and Laura Verde

Electronics 2021, 10(23), 2986; https://doi.org/10.3390/electronics10232986 - 1 Dec 2021

Cited by 2 | Viewed by 1823

Abstract

Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (

T_{i}

A_{i}

O_{i}

) for the i-th subject provide, all together, a

100 %

correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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18 pages, 2638 KiB

Open AccessArticle

IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

by Ioana Apostol, Marius Preda, Constantin Nila and Ion Bica

Electronics 2021, 10(16), 1876; https://doi.org/10.3390/electronics10161876 - 4 Aug 2021

Cited by 44 | Viewed by 5435

Abstract

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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22 pages, 9253 KiB

Open AccessArticle

HadithTrust: Trust Management Approach Inspired by Hadith Science for Peer-to-Peer Platforms

by Amal Alqahtani, Heba Kurdi and Majed Abdulghani

Electronics 2021, 10(12), 1442; https://doi.org/10.3390/electronics10121442 - 16 Jun 2021

Cited by 5 | Viewed by 2967

Abstract

Peer-to-peer (P2P) platforms are gaining increasing popularity due to their scalability, robustness and self-organization. In P2P systems, peers interact directly with each other to share resources or exchange services without a central authority to manage the interaction. However, these features expose P2P platforms to malicious attacks that reduce the level of trust between peers and in extreme situations, may cause the entire system to shut down. Therefore, it is essential to employ a trust management system that establishes trust relationships among peers. Current P2P trust management systems use binary categorization to classify peers as trustworthy or not trustworthy. However, in the real world, trustworthiness is a vague concept; peers have different levels of trustworthiness that affect their overall trust value. Therefore, in this paper, we developed a novel trust management algorithm for P2P platforms based on Hadith science where Hadiths are systematically classified into multiple levels of trustworthiness, based on the quality of narrator and content. To benchmark our proposed system, HadithTrust, we used two state-of-art trust management systems, EigenTrust and InterTrust, with no-trust algorithm as a baseline scenario. Various experimental results demonstrated the superiority of HadithTrust considering eight performance measures. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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26 pages, 1086 KiB

Open AccessFeature PaperArticle

Cyber Resilience Meta-Modelling: The Railway Communication Case Study

by Emanuele Bellini, Stefano Marrone and Fiammetta Marulli

Electronics 2021, 10(5), 583; https://doi.org/10.3390/electronics10050583 - 2 Mar 2021

Cited by 13 | Viewed by 3297

Abstract

Recent times have demonstrated how much the modern critical infrastructures (e.g., energy, essential services, people and goods transportation) depend from the global communication networks. However, in the current Cyber-Physical World convergence, sophisticated attacks to the cyber layer can provoke severe damages to both physical structures and the operations of infrastructure affecting not only its functionality and safety, but also triggering cascade effects in other systems because of the tight interdependence of the systems that characterises the modern society. Hence, critical infrastructure must integrate the current cyber-security approach based on risk avoidance with a broader perspective provided by the emerging cyber-resilience paradigm. Cyber resilience is aimed as a way absorb the consequences of these attacks and to recover the functionality quickly and safely through adaptation. Several high-level frameworks and conceptualisations have been proposed but a formal definition capable of translating cyber resilience into an operational tool for decision makers considering all aspects of such a multifaceted concept is still missing. To this end, the present paper aims at providing an operational formalisation for cyber resilience starting from the Cyber Resilience Ontology presented in a previous work using model-driven principles. A domain model is defined to cope with the different aspects and “resilience-assurance” processes that it can be valid in various application domains. In this respect, an application case based on critical transportation communications systems, namely the railway communication system, is provided to prove the feasibility of the proposed approach and to identify future improvements. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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20 pages, 5259 KiB

Open AccessArticle

An Efficient and Accurate Depth-Wise Separable Convolutional Neural Network for Cybersecurity Vulnerability Assessment Based on CAPTCHA Breaking

by Stephen Dankwa and Lu Yang

Electronics 2021, 10(4), 480; https://doi.org/10.3390/electronics10040480 - 18 Feb 2021

Cited by 7 | Viewed by 2893

Abstract

Cybersecurity practitioners generate a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) as a form of security mechanism in website applications, in order to differentiate between human end-users and machine bots. They tend to use standard security to implement CAPTCHAs in order to prevent hackers from writing malicious automated programs to make false website registrations and to restrict them from stealing end-users’ private information. Among the categories of CAPTCHAs, the text-based CAPTCHA is the most widely used. However, with the evolution of deep learning, it has been so dramatic that tasks previously thought not easily addressable by computers and used as CAPTCHA to prevent spam are now possible to break. The workflow of CAPTCHA breaking is a combination of efforts, approaches, and the development of the computation-efficient Convolutional Neural Network (CNN) model that attempts to increase accuracy. In this study, in contrast to breaking the whole CAPTCHA images simultaneously, this study split four-character CAPTCHA images for the individual characters with a 2-pixel margin around the edges of a new training dataset, and then proposed an efficient and accurate Depth-wise Separable Convolutional Neural Network for breaking text-based CAPTCHAs. Most importantly, to the best of our knowledge, this is the first CAPTCHA breaking study to use the Depth-wise Separable Convolution layer to build an efficient CNN model to break text-based CAPTCHAs. We have evaluated and compared the performance of our proposed model to that of fine-tuning other popular CNN image recognition architectures on the generated CAPTCHA image dataset. In real-time, our proposed model used less time to break the text-based CAPTCHAs with an accuracy of more than 99% on the testing dataset. We observed that our proposed CNN model has efficiently improved the CAPTCHA breaking accuracy and streamlined the structure of the CAPTCHA breaking network as compared to other CAPTCHA breaking techniques. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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23 pages, 2023 KiB

Open AccessArticle

Neural Networks for Driver Behavior Analysis

by Fabio Martinelli, Fiammetta Marulli, Francesco Mercaldo and Antonella Santone

Electronics 2021, 10(3), 342; https://doi.org/10.3390/electronics10030342 - 1 Feb 2021

Cited by 8 | Viewed by 3516

Abstract

The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. With the purpose to provide an architecture to increase safety and security in automotive context, in this paper we propose a fully connected neural network architecture considering position-based features aimed to detect in real-time: (i) the driver, (ii) the driving style and (iii) the path. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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20 pages, 811 KiB

Open AccessFeature PaperArticle

Blockchain-Based Reputation Systems: Implementation Challenges and Mitigation

by Ammar Battah, Youssef Iraqi and Ernesto Damiani

Electronics 2021, 10(3), 289; https://doi.org/10.3390/electronics10030289 - 26 Jan 2021

Cited by 21 | Viewed by 4823

Abstract

Reputation expresses the beliefs or opinions about someone or something that are held by an individual or by a community. Reputation Management Systems (RMSs) handle representation, computation, and storage of reputation in some quantitative form, suitable for grounding trust relations among parties. Quantifying reputation is important in situations, like online service provision, which involve interaction between parties who do not know (and potentially distrust) each other. The basic idea is to let parties rate each other. When a party is considered for interaction, its ratings can be aggregated in order to derive a score for deciding whether to trust it or not. While much valuable research work has been done on reputation-based trust schemes, the problem of establishing collective trust in the reputation management system itself has never been fully solved. Recently, several researchers have put forward the idea of using Distributed Ledger Technology (DLT) as the foundation for implementing trustworthy RMSs. The purpose of this paper is to identify some critical problems that arise when DLTs are used in order to manage evidence about previous interaction and compute reputations. The paper proposes some practical solutions and describes methods to deploy them on top of standard DLT of the Ethereum family. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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20 pages, 447 KiB

Open AccessArticle

Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data

by Abdulmohsen Almalawi, Adil Fahad, Zahir Tari, Asif Irshad Khan, Nouf Alzahrani, Sheikh Tahir Bakhsh, Madini O. Alassafi, Abdulrahman Alshdadi and Sana Qaiyum

Electronics 2020, 9(6), 1017; https://doi.org/10.3390/electronics9061017 - 18 Jun 2020

Cited by 11 | Viewed by 3201

Abstract

Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms. Full article

(This article belongs to the Special Issue Security and Trust in Next Generation Cyber-Physical Systems)

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