Interactive exploration of data traffic with Hierarchical Network Maps.

Area of Science:

• Computer Science
• Information Visualization
• Network Security

Background:

• Network communication is vital but increasingly vulnerable to cyber threats like malicious code and unauthorized access.
• Traditional methods for monitoring large network systems rely on analyzing system logs for anomaly detection.
• Existing analysis methods often lack the depth needed to fully understand complex network flow behaviors.

Purpose of the Study:

• To introduce Hierarchical Network Map, an interactive visualization technique for deeper insight into network flow behavior.
• To enhance conventional statistical or machine learning-based analysis methods.
• To facilitate user-driven visual exploration for improved network monitoring and security.

Main Methods:

• Utilizing multidimensional modeling combined with position and display awareness.
• Viewing network source and target data hierarchically.
• Implementing interactive features for changing aggregation levels and applying filters.
• Integrating data warehouse technology, information visualization, and decision support.

Main Results:

• The Hierarchical Network Map provides a deeper insight into network flow behavior through user-driven exploration.
• The technique allows for efficient data collection and aggregation over very large datasets.
• Interactive visualization facilitates analytical reasoning and enhances anomaly detection capabilities.

Conclusions:

• Hierarchical Network Map offers an effective enhancement to traditional network security analysis methods.
• The interdisciplinary approach enables efficient data handling and insightful visualization for complex network data.
• This interactive visualization technique improves the ability to detect and understand network anomalies.