Enhancing Cyber Situational Awareness Through Dynamic Adaptive Symbology: The DASS Framework.

Area of Science:

• Cybersecurity
• Military Information Systems
• Human-Computer Interaction

Background:

• Traditional military symbology (e.g., MIL-STD-2525D) is static, limiting real-time threat detection in dynamic cybersecurity operations.
• Existing systems struggle to convey evolving threat severity and context effectively, increasing operator cognitive load.

Purpose of the Study:

• To introduce the Dynamic Adaptive Symbol System (DASS), a novel framework designed to enhance cyber situational awareness.
• To overcome the limitations of static military symbology in military and enterprise cybersecurity environments.

Main Methods:

• Developed a modular Python 3.10 architecture for the DASS.
• Integrated machine learning for dynamic threat detection and symbol adaptation based on severity and context.
• Conducted empirical testing comparing DASS against MIL-STD-2525D with cybersecurity professionals.

Main Results:

• DASS significantly improved threat identification rates by 30%.
• Response times were reduced by 25% with DASS.
• Achieved 90% accuracy in symbol interpretation, prioritizing critical threats and reducing cognitive load.

Conclusions:

• The DASS framework effectively enhances cyber situational awareness by dynamically adapting symbology.
• The system demonstrates superior performance over traditional static symbology in threat detection and response.
• DASS shows promise for military and enterprise cybersecurity operations, despite current focus on virus-based scenarios.