Document Type

Article

Publication Date

2025

DOI

10.3390/s25206300

Publication Title

Sensors

Volume

25

Issue

20

Pages

6300

Abstract

The static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations. This research introduces the Dynamic Adaptive Symbol System (DASS), a novel framework enhancing cyber situational awareness in military and enterprise environments. The DASS addresses static symbology limitations by employing a modular Python 3.10 architecture that uses machine learning-driven threat detection to dynamically adapt symbol visualization based on threat severity and context. Empirical testing assessed the DASS against a MIL-STD-2525D baseline using active cybersecurity professionals. Results show that the DASS significantly improves threat identification rates by 30% and reduces response times by 25%, while achieving 90% accuracy in symbol interpretation. Although the current implementation focuses on virus-based scenarios, the DASS successfully prioritizes critical threats and reduces operator cognitive load.

Rights

© 2025 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The raw data supporting the conclusions of this article will be made available by the authors on request."

Original Publication Citation

Macrino, N., Pallas Enguita, S., & Chen, C. H. (2025). Enhancing cyber situational awareness through dynamic adaptive symbology: The DASS framework. Sensors 25(20), Article 6300. https://doi.org/10.3390/s25206300

ORCID

0009-0009-5048-3964 (Enguita), 0000-0002-4860-9187 (Chen)

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