I Know What You Did Last Summer: Identifying VR User Activity Through VR Network Traffic
Sheikh Samit Muhaimin, Spyridon Mastorakis

TL;DR
This study demonstrates that machine learning models can accurately identify VR applications and user activities from encrypted network traffic, raising privacy concerns in VR environments.
Contribution
It introduces a method to identify VR applications and user activities from network traffic with high accuracy, even with limited data, highlighting privacy vulnerabilities.
Findings
VR application identification accuracy: 92.4%
VR activity recognition accuracy: 91%
Effective with less than 10 minutes of data per application
Abstract
Virtual Reality (VR) technology has gained substantial traction and has the potential to transform a number of industries, including education, entertainment, and professional sectors. Nevertheless, concerns have arisen about the security and privacy implications of VR applications and the impact that they might have on users. In this paper, we investigate the following overarching research question: can VR applications and VR user activities in the context of such applications (e.g., manipulating virtual objects, walking, talking, flying) be identified based on the (potentially encrypted) network traffic that is generated by VR headsets during the operation of VR applications? To answer this question, we collect network traffic data from 25 VR applications running on the Meta Quest Pro headset and identify characteristics of the generated network traffic, which we subsequently use to…
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