Philip Empl, David Koch, Marietheres Dietz, Günther Pernul
{"title":"Digital Twins in Security Operations: State of the Art and Future Perspectives","authors":"Philip Empl, David Koch, Marietheres Dietz, Günther Pernul","doi":"10.1145/3746279","DOIUrl":null,"url":null,"abstract":"In an era of rapid technological advancements, digital twins are gaining attention in industry and research. These virtual representations of real-world entities, enabled by the Internet of Things (IoT), offer advanced simulation and analysis capabilities. Their application spans various sectors, from smart manufacturing to healthcare, highlighting their versatility. However, the rise of digital technologies has also escalated cybersecurity concerns. Historical cyberattacks underscore the urgency for enhanced security operations. In this context, digital twins represent a novel approach to cybersecurity. Industry and academic research are increasingly exploring their potential to protect their assets. Despite growing interest and applications, more comprehensive research synthesis needs to be done, particularly in security operations based on digital twins. Our paper aims to fill this gap through a structured literature review aggregating knowledge from 201 publications. We focus on defining the digital twin in cybersecurity, exploring its applications, and outlining implementations and challenges. To maintain transparency, our data is documented and is publicly available. This survey serves as a crucial guide for academic and industry stakeholders, fostering digital twins in security operations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"69 1","pages":""},"PeriodicalIF":28.0000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3746279","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
In an era of rapid technological advancements, digital twins are gaining attention in industry and research. These virtual representations of real-world entities, enabled by the Internet of Things (IoT), offer advanced simulation and analysis capabilities. Their application spans various sectors, from smart manufacturing to healthcare, highlighting their versatility. However, the rise of digital technologies has also escalated cybersecurity concerns. Historical cyberattacks underscore the urgency for enhanced security operations. In this context, digital twins represent a novel approach to cybersecurity. Industry and academic research are increasingly exploring their potential to protect their assets. Despite growing interest and applications, more comprehensive research synthesis needs to be done, particularly in security operations based on digital twins. Our paper aims to fill this gap through a structured literature review aggregating knowledge from 201 publications. We focus on defining the digital twin in cybersecurity, exploring its applications, and outlining implementations and challenges. To maintain transparency, our data is documented and is publicly available. This survey serves as a crucial guide for academic and industry stakeholders, fostering digital twins in security operations.
期刊介绍:
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.