A Survey on Immersive Cyber Situational Awareness Systems
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Abstract (arXiv)
Cyber situational awareness systems are increasingly used for creating cyber common operating pictures for cybersecurity analysis and education. However, these systems face data occlusion and convolution issues due to the burgeoning complexity, dimensionality, and heterogeneity of cybersecurity data, which damages cyber Situational Awareness (SA) of end-users. Moreover, conventional ways of human-computer interactions, such as mouse and keyboard, increase the mental effort and cognitive load of cybersecurity practitioners, when analyzing cyber situations of large-scale infrastructures. Therefore, immersive technologies, such as virtual reality, augmented reality, and mixed reality, are employed in the cybersecurity realm to create intuitive, engaging, and interactive cyber common operating pictures. The Immersive Cyber Situational Awareness (ICSA) systems provide several unique visualization techniques and interaction features for the perception, comprehension, and projection of cyber SA. However, there has been no attempt to comprehensively investigate and classify the existing state of the art in the use of immersive technologies for cyber SA. Therefore, in this paper, we have gathered, analyzed, and synthesized the existing body of knowledge on ICSA systems. In particular, our survey has identified visualization and interaction techniques, evaluation mechanisms, and different levels of cyber SA (i.e., perception, comprehension, and projection) for ICSA systems. Consequently, our survey has enabled us to propose: (i) a reference framework for designing and analyzing ICSA systems by mapping immersive visualization and interaction techniques to the different levels of ICSA; (ii) future research directions for advancing the state-of-the-art on ICSA systems; and (iii) an in-depth analysis of the industrial implications of ICSA systems to enhance cybersecurity operations.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2024.
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Citing papers per year: 2025: 2; 2026: 4.
6 papers.
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- 2026Australian Bushfire Intelligence with AI-Driven Environmental Analyticscited by 3
- 2026Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learningcited by 3
- 2026AgenticVM: Agentic AI for Adaptive Software Vulnerability Managementcited by 2
- 2026Comparative Analysis of Large Language Models in Healthcarecited by 1
- 2026AgenticVM: Agentic AI for Adaptive Software Vulnerability Management3 May 2026
- 2026Comparative Analysis of Large Language Models in Healthcare11 Apr 2026
- 2026Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning6 Apr 2026
- 2026Australian Bushfire Intelligence with AI-Driven Environmental Analytics3 Jan 2026
- 2025SCALAR: Self-Calibrating Adaptive Latent Attention Representation Learning18 Oct 2025
- 2025RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets14 Sep 2025