A comprehensive survey on modeling of cyber‐physical systems

Graja, Imen; Kallel, Slim; Guermouche, Nawal; Cheikhrouhou, Saoussen; Kacem, Ahmed Hadj

doi:10.1002/cpe.4850

Cited by 36 publications

(14 citation statements)

References 99 publications

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“…CPPS models were classified into time-driven and event-driven systems in [36]. A more generic classification of CPS models was presented in [37], which is divided into three main categories: modeling techniques, modeling requirements, and application domains.…”

Section: A Categories Of Cpps Modelsmentioning

confidence: 99%

A Survey of Cyber-Physical Power System Modeling Methods for Future Energy Systems

2022

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The grid of the future will have a higher penetration of grid edge devices that enable increased automation and grid edge intelligence. The current grid models do not account for these gridedge devices, and the creation of cyber-physical models for the grid is essential to understand the impact of these devices. Although existing cyber-physical power system (CPPS) models have been developed using a wide variety of approaches, a comprehensive review of the validity of these approaches and their suitability for modeling the future grid has not been performed. In a CPPS, the physical layer usually consists of the power grid and protection devices, whereas the cyber layer consists of communication, computation, and control components. This paper provides a review on the existing approaches to model CPPS and to characterize the inter-and intra-actions for distributed autonomous systems. The CPPS models can then be used to perform various analyses, such as cyberattack analysis, threat analysis, and resilience analysis. A qualitative evaluation criteria for the various modeling paradigm is discussed to help researchers understand the trade-offs in choosing the right modeling method for their particular application.INDEX TERMS Cyber-physical power systems (CPPS), cybersecurity, finite state machines, graph network, modeling techniques, system and control method, test beds. I. INTRODUCTIONP OWER grid modernization has gained significant momentum in the last decade. As part of the modernization, advanced communication and automation technologies are being deployed in power systems, and the resulting systems are known as cyber-physical power systems (CPPS), which consist of physical (the power grid) and cyber (e.g., communication and computation systems) layers [1]. CPPS leverage two-way cyber-secure communication systems to improve the monitoring, protection, and control of power system components to achieve a smart grid concept with enhanced reliability, resilience, security, and sustainability [2], [3]. Although advanced technologies in the cyber layer improve the operation and control of power systems, they can expose power systems to multiple types of cyber and cyber-physical attacks [4], [5]. The increase of cyber threats can jeopardize the power system's ability to provide reliable and efficient power supply [6]; therefore, accurate and detailed modeling of CPPS and the dependencies between the physical and cyber systems in a CPPS is a necessary step toward the analysis, evaluation, and enhancement of the CPPS's reliability and resilience. A. RELATED WORKExtensive reviews on cyber-physical systems (CPS)-in particular, CPPS-modeling, analyses, evaluations and enhancement methods have been conducted [7]- [10]. The authors of [7] provided a review on the architectural model-

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Section: A Categories Of Cpps Modelsmentioning

confidence: 99%

A Survey of Cyber-Physical Power System Modeling Methods for Future Energy Systems

2022

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“…The adoption of hashing is witnessed in improving security over different application variants [31], [32], [33], [34], [35]. The hashing algorithm effectively identifies and validates that a legal person forwards the data received by a user, or the received information is authentic and not altered [36].…”

Section: Sha Familymentioning

confidence: 99%

“…A few years later, on October 2, 2012, the Keccak (as SHA-3) was announced as the competition winner. In 2015, NIST recognized SHA-3 as a standard hash function [32], [33]. Its operation is shown in Fig.…”

Section: Secure Hash Algorithmsmentioning

confidence: 99%

Evolution and Analysis of Secured Hash Algorithm (Sha) Family

Khan

Olanrewaju

Morshidi

et al. 2022

MJCS

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With the rapid advancement of technologies and proliferation of intelligent devices, connecting to the internet challenges have grown manifold, such as ensuring communication security and keeping user credentials secret. Data integrity and user privacy have become crucial concerns in any ecosystem of advanced and interconnected communications. Cryptographic hash functions have been extensively employed to ensure data integrity in insecure environments. Hash functions are also combined with digital signatures to offer identity verification mechanisms and non-repudiation services. The federal organization National Institute of Standards and Technology (NIST) established the SHA to provide security and optimal performance over some time. The most well-known hashing standards are SHA-1, SHA-2, and SHA-3. This paper discusses the background of hashing, followed by elaborating on the evolution of the SHA family. The main goal is to present a comparative analysis of these hashing standards and focus on their security strength, performance and limitations against common attacks. The complete assessment was carried out using statistical analysis, performance analysis and extensive fault analysis over a defined test environment. The study outcome showcases the issues of SHA-1 besides exploring the security benefits of all the dominant variants of SHA-2 and SHA-3. The study also concludes that SHA-3 is the best option to mitigate novice intruders while allowing better performance cost-effectively.

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“…The task to model dynamic relationships between u and y in terms of u A and y A (or u B and y B etc) is the central problem of system identification [13]. The task to model and control one CPS in terms of the relationships between u A , y A , x A and sometimes also G A is more complex [14] and typically involves hybrid models with state-dependent dynamic descriptions. This is a central problem in automatic control and CPS engineering.…”

Section: Interoperability Modelmentioning

confidence: 99%

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Nilsson

Sandin

Delsing

2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.

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A comprehensive survey on modeling of cyber‐physical systems

Cited by 36 publications

References 99 publications

A Survey of Cyber-Physical Power System Modeling Methods for Future Energy Systems

A Survey of Cyber-Physical Power System Modeling Methods for Future Energy Systems

Evolution and Analysis of Secured Hash Algorithm (Sha) Family

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

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