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Detecting framerate-oriented cyber attacks on user experience in virtual reality

Detecting framerate-oriented cyber attacks on user experience in virtual reality

Odeleye, Blessing, Loukas, George ORCID: 0000-0003-3559-5182, Heartfield, Ryan and Spyridonis, Fotios ORCID: 0000-0003-4253-365X (2021) Detecting framerate-oriented cyber attacks on user experience in virtual reality. In: VR4Sec: 1st International Workshop on Security for XR and XR for Security. VR4Sec. (In Press)

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Abstract

Virtual Reality (VR) is expected to become an enabling technology for training in realistic conditions, data visualisation, education and many other applications. However, there is still limited research on cyber threats to VR environments and even less on technical protections against them. We are currently developing a VR testbed specifically designed for assessing different cyber threats, their impact to user experience and corresponding defences. In this work in progress, we demonstrate two novel approaches by which a cyber attack can potentially cause VR sickness on demand based on frame rate manipulation by taking advantage of GPU and network vulnerabilities. We further show that a simple unsupervised machine learning method using Isolation Forest can provide early warning of such attacks likely before they have significant impact on the VR system and its user.

Item Type: Conference Proceedings
Title of Proceedings: VR4Sec: 1st International Workshop on Security for XR and XR for Security
Uncontrolled Keywords: Virtual reality, cyber security, intrusion detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Internet of Things and Security (ISEC)
Last Modified: 29 Jul 2021 09:57
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/33194

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