Abstract
TraceCam introduces a new paradigm in network path analysis, leveraging GPU-accelerated WebGL visualization, advanced traceroute integrations, and AI-driven insights to transform complex routing data into actionable intelligence. Early prototypes have demonstrated significant improvements in performance, clarity, and multi-path discovery, overcoming traditional limitations in traceroute analysis. By incorporating retrieval-augmented language models and enriched metadata sources like IPinfo.io, TraceCam enables automated anomaly detection, contextual explanations, and rapid root-cause analysis, enhancing operational efficiency. The platform’s architecture ensures scalability and adaptability, supporting deeper investigations and real-time situational awareness. Future development will focus on clustering-based anomaly detection, expanded geographic visualizations, and enhanced AI-generated analysis to further streamline network diagnostics and security response.
Recommended Citation
Makowski, Cameron
(2025)
"Network and Multipath Traceroute Visualization,"
Military Cyber Affairs: Vol. 8
:
Iss.
1
, Article 9.
Available at:
https://digitalcommons.usf.edu/mca/vol8/iss1/9
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