Adapting level of detail in user interfaces for Cybersecurity operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As cybersecurity threats increasingly appear in news headlines, the security industry continues to build state of the art firewall and intrusion detection systems for monitoring activities in complex cyber networks. These systems generate millions of log files and continuous alerts. In order to make sense of cyber data, cyber security and system administrators review and analyze millions of logs using highly summarized views and manual cycles of click-intensive details-on-demand. This is laborious, induces cognitive overload, and is prone to errors resulting in important information and impacts not being seen when most needed. Our research focus is on developing “FocalPoint” a system that provides Adaptive Level of Detail (LOD) in user interfaces for cybersecurity operations. FocalPoint is a recommender system tailored for complex network information structures that reasons about contextual information associated with the network, user tasks, and cognitive load. This facilitates tuning cyber visualization displays thereby improving user performance in perception, comprehension and projection of current Cybersecurity Situational Awareness (Cyber SA). For cyber analysts, having the right information, in context, when most needed without cognitive overload could lead to effective decision making in cyber operations. We provide a use case scenario for FocalPoint with an in-progress prototype and highlight various challenges and potential considerations for building an effective adaptive system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it