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Digital Forensics for Analyzing Cyber Threats in the XR Technology Ecosystem within Digital Twinsoa mark
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Publication Year
2024-07-01
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Electronics (Switzerland), Vol.13
Keyword
cybersecuritydigital forensicsdigital twinindustrial control systemXR devices
All Science Classification Codes (ASJC)
Control and Systems EngineeringSignal ProcessingHardware and ArchitectureComputer Networks and CommunicationsElectrical and Electronic Engineering
Abstract
Recently, advancements in digital twin and extended reality (XR) technologies, along with industrial control systems (ICSs), have driven the transition to Industry 5.0. Digital twins mimic and simulate real-world systems and play a crucial role in various industries. XR provides innovative user experiences through virtual reality (VR), augmented reality (AR), and mixed reality (MR). By integrating digital twin simulations into XR devices, these technologies are utilized in various industrial fields. However, the prevalence of XR devices has increased the exposure to cybersecurity threats in ICS and digital twin environments. Because XR devices are connected to networks, the control and production data they process are at risk of being exposed to cyberattackers. Attackers can infiltrate XR devices through malicious code or hacking attacks to take control of the ICS or digital twin or paralyze the system. Therefore, this study emphasizes the cybersecurity threats in the ecosystem of XR devices used in ICSs and conducts research based on digital forensics. It identifies potentially sensitive data and artifacts in XR devices and proposes secure and reliable security response measures in the Industry 5.0 environment.
ISSN
2079-9292
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/34322
DOI
https://doi.org/10.3390/electronics13132653
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Type
Article
Funding
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MIST) (No. 2022-0-01022, Development of Collection and Integrated Analysis Methods of Automotive Inter/Intra System Artifacts through Construction of Event-based experimental system).
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SHON, TAE SHIK Image
SHON, TAE SHIK손태식
Department of Cyber Security
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