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AI and Security in Cyber Physical System Design

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 27578

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Special Issue Editors

Dr. Anikó Costa

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Guest Editor

Faculty of Sciences and Technology, Department of Electrical and Computer Engineering, NOVA University of Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal
Interests: model-based development; petri-nets; co-design of embedded systems; cyber–physical systems; reconfigurable computing platforms; FPGAs
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Remigiusz Wiśniewski

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Co-Guest Editor

Institute of Electrical Engineering, University of Zielona Góra, Zielona Gora, Poland
Interests: cyber-physical systems; Petri nets; discrete-event systems; programmable devices; field programmable gate arrays (FPGAs); partial reconfiguration of FPGAs; hardware description languages (Verilog, VHDL); cryptography and cryptology (prime numbers, RSA-numbers, factorization, development of algorithms, OTP-based ciphers)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cyber-physical systems (CPS) can be seen as the combination of computation, communication, and physical processes, where embedded computation and networking can control physical devices.

The combination of machines, sensors, embedded computational intelligence, and communication mechanisms allow CPS to monitor and control physical components through computer-based algorithms strongly supported by internet connectivity (Internet of Things—IoT). New sensors with computational capabilities allowing adaptive and cognitive computation are becoming the core of CPS and safety-critical systems.

Cyber-physical systems also play an essential role in industrial digitalization. Associated processes require automatized data acquisition and data storage, beyond analyses on the cloud. Having secure communication between physical devices and the cloud is a vital issue.

Continuous improvements in computational methods, artificial intelligence (AI), and communications, as well as at sensor and actuators levels, allow machines to refine their performance interacting with the surrounding environment. Adaptation and self-learning need to be present whenever fault tolerant operation and resiliency need to be considered. Cybersecurity in operation is mandatory whenever control of critical infrastructures is considered, leading to high security requirements.

Summarizing the above, it can be noticed that the current design of cyber-physical systems has to face numerous challenges when integrating the new AI methodologies, keeping security and resiliency at high levels.

Topics of interest include but are not limited to:

Design methodologies of CPS and IoT;
Formal analysis and verification of CPS and IoT, safety-critical systems;
Cybersecurity aspects, including cryptographic algorithms, protocols, e-services, etc.;
Artificial Intelligence, machine learning, and deep learning techniques applied to CPS and IoT;
AI-based security solutions for CPS and IoT;
Impact of AI-based solutions on CPS performance;
Modeling of attack behavior;
Attack prediction modeling;
Industrial digitalization and CPS.

Dr. Anikó Costa
Prof. Dr. Remigiusz Wiśniewski
Guest Editors

Manuscript Submission Information

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Keywords

cyber-physical systems
Internet of Things
safety
security
embedded computing
sensors networks
artificial intelligence

Published Papers (9 papers)

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Research

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18 pages, 1979 KiB

Open AccessArticle

A Novel Administration Model for Managing and Organising the Heterogeneous Information Security Policy Field

by Fahad Mazaed Alotaibi, Arafat Al-Dhaqm, Wael M. S. Yafooz and Yasser D. Al-Otaibi

Appl. Sci. 2023, 13(17), 9703; https://doi.org/10.3390/app13179703 - 28 Aug 2023

Cited by 3 | Viewed by 917

Abstract

Information security policy (ISP) plays a crucial role in maintaining the availability, confidentiality, and integrity of sensitive data. However, it is of high complexity and heterogeneity due to the variety and redundancy of security policy practices and complexity of organisational systems. Various and duplicate ISP models and frameworks have been offered in the literature. The duplicate security policy practices, procedures, and processes in the existing models have made ISP disorganised, unstructured, and unclear to organisational users. As a result, there is still a need for a standardised and integrated model to make it simpler to share, manage, and reuse ISP practices amongst the organisations. The main objective of this study is to construct a metamodel to unify, organise, and structure ISP practices. By identifying, recognising, extracting, and combining the common information security policy practices from various ISP models in a built ISP metamodel called ISPM, we seek to make it simple for users and field specialists to derive/instantiate security policy models for their organisations. The development and validation process of the ISPM is based on the common security frameworks such as ISO 27001 frameworks. The developed ISPM consists of 19 common security practices: organisation, risk management, access control policy, edit, review, compliance, business management, backup and recovery, incident response, SETA program, security awareness, security training, security education, email security policy, cloud security policy, network security policy, website security policy, physical security policy, and privacy security policy. Each common security practice consists of several operations and attributes. The performance of the developed ISPM was compared to that of other models to evaluate its completeness and logicalness. Using ISO 27001 as a framework, the findings confirmed the comprehensiveness of ISPM. Therefore, it can contribute to organisations’ security by helping them to develop their own security policy models. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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15 pages, 3267 KiB

Open AccessArticle

Decomposition of a Petri Net-Based Cyber-Physical System toward Implementation as an Integrated System within FPGA

by Remigiusz Wiśniewski, Anikó Costa, Marcin Wojnakowski and Maxim Maliński

Appl. Sci. 2023, 13(12), 7137; https://doi.org/10.3390/app13127137 - 14 Jun 2023

Viewed by 652

Abstract

Decomposition is one of the commonly used techniques applied in the design of Petri net-based cyber-physical systems. Such an operation permits the splitting of the initial system into sequential components that can be further implemented as an integrated or distributed system. This paper is focused on the decomposition of the modelled CPS toward its further implementation as an integrated system, namely an FPGA device. The adequate decomposition method is presented and explained in detail. Moreover, the proposed idea is explained by the real-life example of the beverage production and distribution system. The results of the experiments are presented and discussed. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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19 pages, 9951 KiB

Open AccessArticle

A Secure Real-Time IoT Data Stream Based on Improved Compound Coupled Map Lattices

by Shyi-Tsong Wu

Appl. Sci. 2022, 12(17), 8489; https://doi.org/10.3390/app12178489 - 25 Aug 2022

Cited by 1 | Viewed by 1027

Abstract

A secure data stream is important for the real time communications of Internet of Things (IoT). A stream cipher with the characteristics of simple and high speed is suitable in the secure communications of IoT for its security. Some stream ciphers based on coupled map lattices (CML) were proposed. However, the original coupled map lattice shows evidence of correlation between the contiguous points. In this paper, we present an improved CML with a counter to overcome the weakness. The proposed scheme has the advantage of simplicity and suits the resource constrained IoT environment. We implement the proposed improved CML and analyze the proposed ciphers against some attacks. For the balance performance analysis, the numbers of 0 and 1 in the keystream are almost balanced, and the difference rates show that the proposed schemes have high key sensitivity. Finally, we present some experimental results of statistical random number tests for the output keystreams. Under the pass rates of the statistical test of NIST SP800-22, the proposed ciphers with improved CML, and compound CML are at least 95% and 97%, respectively. All the pass rates of the proposed stream ciphers are 100% for the statistical test of FIPS PUB 140-1. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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13 pages, 1462 KiB

Open AccessArticle

Privacy-by-Design and Minimization within a Small Electronic Health Record: The Health360 Case Study

by Raffaele Conte, Francesco Sansone, Alessandro Tonacci and Anna Paola Pala

Appl. Sci. 2022, 12(17), 8441; https://doi.org/10.3390/app12178441 - 24 Aug 2022

Cited by 5 | Viewed by 1170

Abstract

Electronic health records are playing an important role in todays’ clinical research, with the possibility to collect a wide amount of data from different sources, not only within a structured clinical setting, but also making best use of new portable technologies, such as smartphones, sensors and Internet-of-Things, as an unprecedented spring of data. In this way, even in small clinical centers, often featuring limited financial availabilities, not only clinicians can have a complete, timely outlook on patients’ health, but also data scientists could use such information to build and train tailored models in the broader perspective of “p4 medicine”. However, all this should align with the strict regulations and needs concerning data privacy and security, safeguarding the rights of the individual and the confidentiality of information related to their healthcare status. Here, we present a case study dealing with Health360, a platform designed to fill in this gap, representing the ideal solution for small clinical centers, where usability and cost-affordability are key characteristics for such a system, to collect multimodal data from various sources actually employed in the framework of neuromuscular conditions. The platform, designed under the Software-as-a-Service paradigm, actually collects data from different clinical centers active in the field of neuromuscular diseases, and therefore was designed to grant access to the data to specific professionals depending on their roles. At the same time, to the benefit of data scientists, Health360 enables joint data processing, with the management of authorization principles for various health professionals from different clinical centers, which is regulated by the data minimization principle, based on the accessing profile. Under such premises, we present here the approach followed for the implementation of the platform, managing the trade-off between the need from various professionals for accessing the complete dataset and the privacy requirements, as well as confidentiality maintenance for sensitive data of patients enrolled on the project. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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23 pages, 532 KiB

Open AccessArticle

An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture

by Zeeshan Ali Khan, Ubaid Abbasi and Sung Won Kim

Appl. Sci. 2022, 12(6), 3163; https://doi.org/10.3390/app12063163 - 20 Mar 2022

Cited by 4 | Viewed by 2278

Abstract

Network-on-chip (NoC) is replacing the existing on-chip communication mechanism in the latest, very-large-scale integration (VLSI) systems because of their fault tolerant design. However, in addition to the design challenges, NoC systems require a mechanism for proper application mapping in order to produce maximum benefits in terms of application-level latency, platform energy consumption, and system throughput. Similarly, the neural-network (NN)-based artificial intelligence (AI) techniques for deep learning are gaining particular interest. These applications can be executed on a cloud-based system, but some of these applications have to be executed on private cloud to integrate the data privacy. Furthermore, the public cloud systems can also be made from these NoC platforms to have better application performance. Therefore, there is a need to optimally map these applications on existing NoC-based architectures. If the application is not properly mapped, then it can create a performance hazard that may lead to delay in calculations, increase in energy consumption, and decrease in the platform lifetime. Hence, the real-time applications requiring AI services can implement these algorithms in NoC-based architectures with better real-time performance. In this article, we propose a multilevel mapping of deep learning AI applications on the NoC architectures and show its results for the energy consumption, task distribution profile, latency, and throughput. The simulation is conducted using the OCTAVE, and the simulation results show that the performance of the proposed mapping technique is better than the direct mapping techniques. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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25 pages, 3508 KiB

Open AccessArticle

Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives

by Roberto Casadei, Danilo Pianini, Mirko Viroli and Danny Weyns

Appl. Sci. 2022, 12(1), 349; https://doi.org/10.3390/app12010349 - 30 Dec 2021

Cited by 9 | Viewed by 1999

Abstract

The engineering of large-scale cyber-physical systems (CPS) increasingly relies on principles from self-organisation and collective computing, enabling these systems to cooperate and adapt in dynamic environments. CPS engineering also often leverages digital twins that provide synchronised logical counterparts of physical entities. In contrast, sensor networks rely on the different but related concept of virtual device that provides an abstraction of a group of sensors. In this work, we study how such concepts can contribute to the engineering of self-organising CPSs. To that end, we analyse the concepts and devise modelling constructs, distinguishing between identity correspondence and execution relationships. Based on this analysis, we then contribute to the novel concept of “collective digital twin” (CDT) that captures the logical counterpart of a collection of physical devices. A CDT can also be “augmented” with purely virtual devices, which may be exploited to steer the self-organisation process of the CDT and its physical counterpart. We underpin the novel concept with experiments in the context of the pulverisation framework of aggregate computing, showing how augmented CDTs provide a holistic, modular, and cyber-physically integrated system view that can foster the engineering of self-organising CPSs. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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19 pages, 1343 KiB

Open AccessArticle

True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA

by Pietro Nannipieri, Stefano Di Matteo, Luca Baldanzi, Luca Crocetti, Jacopo Belli, Luca Fanucci and Sergio Saponara

Appl. Sci. 2021, 11(8), 3330; https://doi.org/10.3390/app11083330 - 07 Apr 2021

Cited by 26 | Viewed by 5092

Abstract

Random numbers are widely employed in cryptography and security applications. If the generation process is weak, the whole chain of security can be compromised: these weaknesses could be exploited by an attacker to retrieve the information, breaking even the most robust implementation of a cipher. Due to their intrinsic close relationship with analogue parameters of the circuit, True Random Number Generators are usually tailored on specific silicon technology and are not easily scalable on programmable hardware, without affecting their entropy. On the other hand, programmable hardware and programmable System on Chip are gaining large adoption rate, also in security critical application, where high quality random number generation is mandatory. The work presented herein describes the design and the validation of a digital True Random Number Generator for cryptographically secure applications on Field Programmable Gate Array. After a preliminary study of literature and standards specifying requirements for random number generation, the design flow is illustrated, from specifications definition to the synthesis phase. Several solutions have been studied to assess their performances on a Field Programmable Gate Array device, with the aim to select the highest performance architecture. The proposed designs have been tested and validated, employing official test suites released by NIST standardization body, assessing the independence from the place and route and the randomness degree of the generated output. An architecture derived from the Fibonacci-Galois Ring Oscillator has been selected and synthesized on Intel Stratix IV, supporting throughput up to 400 Mbps. The achieved entropy in the best configuration is greater than 0.995. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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Review

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20 pages, 2197 KiB

Open AccessReview

Aspects of Cyber Security in Autonomous and Connected Vehicles

by Bhavesh Raju Mudhivarthi, Prabhat Thakur and Ghanshyam Singh

Appl. Sci. 2023, 13(5), 3014; https://doi.org/10.3390/app13053014 - 26 Feb 2023

Cited by 3 | Viewed by 5722

Abstract

An automobile is a computer on wheels after the integration of electronics. This handshake of electronics and mechanical systems makes a vehicle smart, and comfortable; driver assistance for achieving this involves data exchange and surroundings sensing. Devices such as sensors, telematics, protocols, etc., are responsible for data exchange and data sensing. This process contains some loopholes that are the preliminary sources for the attacker to attack the vulnerable devices to control the vehicle. This article provides a review of possible attacks and defenses on autonomous and connected vehicles. The attacker’s area of autonomous and connected vehicles is classified into three categories that are safety system attacks, connectivity attacks, and diagnostics attacks, and provided all possible defenses for those attacks. In addition, we provided an analysis of the domain to understand the scenarios in this domain, recommendations, and future scope in this area for further work. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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30 pages, 2743 KiB

Open AccessFeature PaperReview

A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

by Sangjun Kim and Kyung-Joon Park

Appl. Sci. 2021, 11(12), 5458; https://doi.org/10.3390/app11125458 - 12 Jun 2021

Cited by 25 | Viewed by 5177

Abstract

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks. Full article

(This article belongs to the Special Issue AI and Security in Cyber Physical System Design)

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