Enhancing the Efficacy of Identifying Visual Patterns and Novel Anomalies of Cyber-Defenders with 3D Immersive VR

Abstract Cyber Security Defenders (CSDs) face the critical challenge of identifying anomalies in an increasingly complex and rapidly evolving data landscape during cyberattacks. This study introduces a novel approach to enhance CSDs' ability to detect unknown patterns and novel anomalies using 3D Immersive Virtual Reality (3DIVR) technology, addressing a significant gap in current cyber defense training methodologies. The research uniquely employs a moderated mediation model to examine the effect of 3DIVR on identifying hidden forms in Embedded Figure Tasks (EFTs) and its subsequent impact on novel anomaly detection. This approach is particularly innovative as it challenges the conventional reliance on previous experience, which can potentially hinder a CSD's ability to identify unfamiliar anomaly patterns. Through a quasi-experimental design with repeated measurements, we compared five research groups, four of which underwent cognitive intervention while detecting cyber anomalies. The study involved 120 students from a cyber security defender training program, utilizing a cluster sampling method. Our hypothesis posited that practicing hidden shapes tasks in an information-diluted environment would lead to greater improvements in anomaly detection precision and speed compared to practice in information-laden settings. Key findings reveal that the final course scores were significantly higher among male participants compared to female participants; and that participants who practiced EFTs in an IVR information-diluted environment (VRLVL) demonstrated significantly faster detection of novel anomalies compared to the control group. Additionally, we discovered a positive correlation between cognitive elasticity and anomaly detection speed in participants exposed to an IVR highly-loaded environment (VRHVL). These results offer valuable insights into optimizing cyber defense training protocols. The study's utility lies in its potential to revolutionize CSD training methodologies, enhancing defenders' adaptability to emerging threats in an ever-changing cybersecurity landscape. By leveraging 3DIVR technology, this research paves the way for more effective and adaptable cyber defense strategies, crucial in an era of rapidly evolving cyber threats.

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Publication Details

Journal
SN Computer Science
Published
2025-09-30
DOI
https://doi.org/10.1007/s42979-025-04337-y
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhancing the Efficacy of Identifying Visual Patterns and Novel Anomalies of Cyber-Defenders with 3D Immersive VR

Davіd Passіg, Guy Sion, Reut Hochman
SN Computer Science
Data Visualization and Analytics
article

Enhancing the Efficacy of Identifying Visual Patterns and Novel Anomalies of Cyber-Defenders with 3D Immersive VR

Davіd Passіg, Guy Sion, Reut Hochman
article en

Abstract

Abstract Cyber Security Defenders (CSDs) face the critical challenge of identifying anomalies in an increasingly complex and rapidly evolving data landscape during cyberattacks. This study introduces a novel approach to enhance CSDs' ability to detect unknown patterns and novel anomalies using 3D Immersive Virtual Reality (3DIVR) technology, addressing a significant gap in current cyber defense training methodologies. The research uniquely employs a moderated mediation model to examine the effect of 3DIVR on identifying hidden forms in Embedded Figure Tasks (EFTs) and its subsequent impact on novel anomaly detection. This approach is particularly innovative as it challenges the conventional reliance on previous experience, which can potentially hinder a CSD's ability to identify unfamiliar anomaly patterns. Through a quasi-experimental design with repeated measurements, we compared five research groups, four of which underwent cognitive intervention while detecting cyber anomalies. The study involved 120 students from a cyber security defender training program, utilizing a cluster sampling method. Our hypothesis posited that practicing hidden shapes tasks in an information-diluted environment would lead to greater improvements in anomaly detection precision and speed compared to practice in information-laden settings. Key findings reveal that the final course scores were significantly higher among male participants compared to female participants; and that participants who practiced EFTs in an IVR information-diluted environment (VRLVL) demonstrated significantly faster detection of novel anomalies compared to the control group. Additionally, we discovered a positive correlation between cognitive elasticity and anomaly detection speed in participants exposed to an IVR highly-loaded environment (VRHVL). These results offer valuable insights into optimizing cyber defense training protocols. The study's utility lies in its potential to revolutionize CSD training methodologies, enhancing defenders' adaptability to emerging threats in an ever-changing cybersecurity landscape. By leveraging 3DIVR technology, this research paves the way for more effective and adaptable cyber defense strategies, crucial in an era of rapidly evolving cyber threats.

SN Computer ScienceVol. 6(7)
Bar-Ilan University (IL), Charles Sturt University (AU), David Yellin College of Education (IL)
Openalex Percentile: Top 74%
Data Visualization and Analytics
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