Adaptive Visualization of Complex Networks with FocalPoint
Bibliographic record
Abstract
Maintaining situational awareness of a dynamic global computer network that consists of ten to hundreds of thousands of computers is a complex task for cyber administrators and operators looking to understand, plan and conduct operations in real time. Currently, cyber specialists must manually navigate complex networks by continuous cycles of overviews, drilldowns and manually mapping network incidents to mission impact. This is inefficient as manually maneuvering of network data is laborious, induces cognitive overload, and is prone to errors caused by distractive information resulting in important information and impacts not being seen. We are investigating “FocalPoint” an adaptive level of detail (LOD) recommender system tailored for hierarchical network information structures. FocalPoint reasons about contextual information associated with the network, user task, and user cognitive load to tune the presentation of network visualization displays to improve user performance in perception, comprehension and projection of current situational awareness. Our system is applied to two complex information constructs important to dynamic cyber network operations: network maps and attack graphs. The key innovations include: (a) context-aware automatic tailoring of complex network views, (b) multi-resolution hierarchical graph aggregation, (c) incorporation of new computational models for adaptive-decision making on user tasks, cost/benefit utility and human situation awareness, and (d) user interaction techniques to integrate recommendations into the network viewing system. Our aim is to have a direct impact on planning and operations management for complex networks by; overcoming information overload, preventing tunnel vision, reducing cognitive load, and increasing time available to focus on optimum level of details of the global network space and missions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".