Enhancing cybersecurity situation awareness through visualization: A USB data exfiltration case study
Bibliographic record
Abstract
Employees who have legitimate access to an organization's data may occasionally put sensitive corporate data at risk, either carelessly or maliciously. Ideally, potential breaches should be detected as soon as they occur, but in practice there may be delays, because human analysts are not able to recognize data exfiltration behaviors quickly enough with the tools available to them. Visualization may improve cybersecurity situation awareness. In this paper, we present a dashboard application for investigating file activity, as a way to improve situation awareness. We developed this dashboard for a wide range of stakeholders within a large financial services company. Cybersecurity experts/analysts, data owners, team leaders/managers, high level administrators, and other investigators all provided input to its design. The use of a co-design approach helped to create trust between users and the new visualization tools, which were built to be compatible with existing work processes. We discuss the user-centered design process that informed the development of the dashboard, and the functionality of its three inter-operable monitoring dashboards. In this case three dashboards were developed covering high-level overview, file volume/type comparison, and individual activity, but the appropriate number and type of dashboards to use will likely vary according to the nature of the detection task). We also present two use cases with usability results and preliminary usage data. The results presented examined the amount of use that the dashboards received as well as measures obtained using the Technology Acceptance Model (TAM). We also report user comments about the dashboards and how to improve them.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".