A mixed-methods investigation of XR security warnings: lessons learned
Zou, Junyi, Bovo, Riccardo, Hamza, Ali and Loukas, George ORCID: https://orcid.org/0000-0003-3559-5182
(2025)
A mixed-methods investigation of XR security warnings: lessons learned.
In: International Workshop on Content-Based Multimedia Indexing, CBMI.
Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey.
(In Press)
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50979 ZOU_A_Mixed-Methods_Investigation_Of_XR_Security_Warnings_Lessons_Learned_(AAM)_2025.pdf - Accepted Version Restricted to Repository staff only Download (4MB) | Request a copy |
Abstract
As immersive XR environments become more prevalent, timely and effective security warnings are essential to protect users from cyberattacks that compromise performance and well-being. This paper investigates how users perceive and respond to in-headset alerts triggered during Denial-of-Service (DoS) attacks. We developed a real-time warning system and evaluated its effectiveness across three pilot studies (n = 46) in healthcare and industrial training scenarios. Using self-report measures (IDSQ, SAM) and behavioural categorization, we assessed alert comprehension, urgency perception, and user action. We distil three design lessons emphasizing the importance of visual salience, modality coordination, and urgency calibration. These findings offer practical guidance for designing effective XR security notifications that support user awareness and action during immersive threats.
Item Type: | Conference Proceedings |
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Title of Proceedings: | International Workshop on Content-Based Multimedia Indexing, CBMI |
Uncontrolled Keywords: | Extended Reality (XR), security warnings, intrusion detection, Denial-of-Service (DoS), multimodal alerts, Human-Computer Interaction (HCI), user attention, cybersecurity, immersive environments, usable security |
Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Faculty / School / Research Centre / Research Group: | Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) |
Related URLs: | |
Last Modified: | 04 Sep 2025 09:17 |
URI: | https://gala.gre.ac.uk/id/eprint/50979 |
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