University of Oulu

Korkiakoski, M., Sadiq, F., Setianto, F., Latif, U. K., Alavesa, P., & Kostakos, P. (2021). Using smart glasses for monitoring cyber threat intelligence feeds. Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 630–634. https://doi.org/10.1145/3487351.3492722

Using smart glasses for monitoring cyber threat intelligence feeds

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Author: Korkiakoski, Mikko1; Sadiq, Fatima1; Setianto, Febrian1;
Organizations: 1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022051234817
Language: English
Published: , 2022
Publish Date: 2022-05-12
Description:

Abstract

The surge of COVID-19 has introduced a new threat surface as malevolent actors are trying to benefit from the pandemic. Because of this, new information sources and visualization tools about COVID-19 have been introduced into the workflow of frontline practitioners. As a result, analysts are increasingly required to shift their focus between different visual displays to monitor pandemic related data, security threats, and incidents. Augmented reality (AR) smart glasses can overlay digital data to the physical environment in a comprehensible manner. However, the real-life use situations are often complex and require fast knowledge acquisition from multiple sources. In this study we report results from an experiment with six subjects using an AR overlaid information interface coupled with traditional computer monitors. Our goal was to evaluate a multi tasking setup with traditional monitors and an AR headset where notifications from the new COVID-19 MISP instance were visualized. Our results indicate that better situational awareness does translate to increased task performance, but at the cost of a gender gap that requires further attention.

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ISBN Print: 978-1-4503-9128-3
Pages: 630 - 634
DOI: 10.1145/3487351.3492722
OADOI: https://oadoi.org/10.1145/3487351.3492722
Host publication: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
Host publication editor: Coscia, Michele
Cuzzocrea, Alfredo
Shu, Kai
Conference: 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work has been partially funded by the European Commission grants PRINCE (815362) and IDUNN (101021911); Business Finland project Reboot Finland IoT Factory 33/31/2018; and Academy of Finland 6Genesis Flagship (318927).
EU Grant Number: (101021911) IDUNN - A Cognitive Detection System for Cybersecure Operational Technologies
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021, https://doi.org/10.1145/3487351.3492722.