ELEGANT: Security of Critical Infrastructures With Digital Twins

Sousa, Bruno; Arieiro, Miguel; Pereira, Vasco; Correia, João; Lourenço, Nuno; Cruz, Tiago

doi:10.1109/access.2021.3100708

Cited by 31 publications

(12 citation statements)

References 27 publications

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“…They use distributing supervised machine learning for data processing and for making decisions between the physical layer, edge layer and cloud layer. Additionally, Sousa et al [ 34 ] present an approach to design DTs to secure critical infrastructures. They use high-fidelity replicas of Programmable Logic Controllers (PLC) to mitigate DoS attacks that use flooding and amplification.…”

Section: Digital Twin Designmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

101

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A Digital Twin (DT) is a set of computer-generated models that map a physical object into a virtual space. Both physical and virtual elements exchange information to monitor, simulate, predict, diagnose and control the state and behavior of the physical object within the virtual space. DTs supply a system with information and operating status, providing capabilities to create new business models. In this paper, we focus on the construction of DTs. More specifically, we focus on determining (methodologically) how to design, create and connect physical objects with their virtual counterpart. We explore the problem into several phases: from functional requirement selection and architecture planning to integration and verification of the final (digital) models. We address as well how physical components exchange real-time information with DTs, as well as experimental platforms to build DTs (including protocols and standards). We conclude with a discussion and open challenges.

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Section: Digital Twin Designmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

101

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“…However, one's on-premises implementation can be costly, implying a sizeable CAPEX for whom the return will currently rely on the capacity to intend and deploy an appropriate support infrastructure as well as implement efficient and scalable collection of data and processing mechanisms able to make use of the resources acquired. Sousa et al [2] proposed an off-premises method for designing and deploying Digital Twins to protect critical infrastructures. In order to help the creation and testing of Machine Learning models to counteract security concerns like Denial-of-Service attacks, such Digital Twins are created utilizing real-time, highly accurate duplicates of Programming Logic Controllers.…”

Section: Literature Surveymentioning

confidence: 99%

“…Advanced robotics, which acts as an agent acting that appears in every area of production lines, is equally essential to smart factory. Digital Twin (DT) is attracted increasing scientific attention as a result of the extensive research and development on Industry 4.0 and Artificial Intelligence (AI) [2]. The network and data serve as the foundation of DT as a digital representation of a physical object, the algorithm and modeling serve as the core, and the application and service serve as the application.…”

Section: Introductionmentioning

confidence: 99%

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Padmapriya¹,

Srivenkatesh²

2023

IJACSA

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Digital twin is one of the most important innovations in the Internet of Things (IoT) era and business disruption. Digital twins are a growing technology that bridges the gap between the real and the digital. Home automation in the IoT refers to the practice of automatically managing and monitoring smart home electronics by use of a variety of control system methods. The geysers, refrigerators, fans, lighting, fire alarms, kitchen timers, and other electrical and electronic items in the home can all be managed and monitored with the help of a variety of control methods. Digital twins replicate the physical machine in real time and produce data, such as asset degradation, product performance level that may be used by the predictive maintenance algorithm to identify the product functionality levels. The purpose of this research is to design the framework of Digital Twin using machine learning and state estimation algorithms model to assess and predict home appliances based on the probability rate of smart home system gadgets functionality. The main goal of this research is to create a digital twin for smart home gadgets that are used to monitor the health status of these devices for increasing the life time and to reduce maintenance costs. This research presents a Deep Convolution Neural Network based Logistic Regression Model with Digital Twins (DCNN-LR-DT) for accurate prediction of smart home gadget functionality levels and to predict the threats in advance. The proposed model is compared with the traditional models and the results represent that the proposed model performance is better than traditional models.

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“…It is foreseen in the next decade that DTs will be widely deployed for various industrial applications due to recent advances in parallel computing, solvers, data processing and management tools, big data, and AI [68], [69]. For example, [70] presents an application of real-time and high-fidelity DTs of physical components to develop, train, and validate ML models to secure critical infrastructures.…”

Section: Digital Twin (Dt)mentioning

confidence: 99%

Perspectives on Future Power System Control Centers for Energy Transition

Marot

Kelly²,

Naglič

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Today's power systems are seeing a paradigm shift under the energy transition, sparkled by the electrification of demand, digitalisation of systems, and an increasing share of decarbonated power generation. Most of these changes have a direct impact on their control centers, forcing them to handle weather-based energy resources, new interconnections with neighbouring transmission networks, more markets, active distribution networks, micro-grids, and greater amounts of available data. Unfortunately, these changes have translated during the past decade to small, incremental changes, mostly centered on hardware, software, and human factors. We assert that more transformative changes are needed, especially regarding humancentered design approaches, to enable control room operators to manage the future power system. This paper discusses the evolution of operators towards continuous operation planners, monitoring complex time horizons thanks to adequate real-time automation. Reviewing upcoming challenges as well as emerging technologies for power systems, we present our vision of a new evolutionary architecture for control centers, both at backend and frontend levels. We propose a unified hypervision scheme based on structured decision-making concepts, providing operators with proactive, collaborative, and effective decision support.

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ELEGANT: Security of Critical Infrastructures With Digital Twins

Cited by 31 publications

References 27 publications

Design, Modeling and Implementation of Digital Twins

Design, Modeling and Implementation of Digital Twins

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Perspectives on Future Power System Control Centers for Energy Transition

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