Visualizing Insider USB File Exfiltration Anomalies: A Financial Services Case Study
Bibliographic record
Abstract
Data exfiltration by insiders (or people masquerading as insiders) is a major threat for organizations that store and use highly confidential data. Standard cybersecurity methods using firewalls and perimeter strengthening measures fail against insiders, and machine learning methods for anomaly detection generate large numbers of false alarms. I worked with domain experts in a large Canadian financial services company and developed new security visualization technology through a Participatory Design (PD) approach. The resulting File Activity Real-Time Investigation Dashboard Application (FARIDA) is usable for a range of stakeholders, allowing experts to quickly review large amounts of data and enabling non-technical investigators to participate in early stages of the anomaly detection process. This research is a first step towards more general use of visualization tools to monitor sensitive data, and demonstrates how human factors approaches can beneficially supplement the development of AI methods in cybersecurity, using data exfiltration as an example.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".