Time Dynamic "Allow Listing" For Dropbox
Bibliographic record
Abstract
Dropbox is a popular cloud-based file hosting service that is widely recognized for its robust data consolidation and security features, which allow for seamless data access across various locations. Our research is centered on a groundbreaking method to bolster Dropbox’s security measures by minimizing the vulnerability window of network ports. This is achieved through the implementation of a dynamic allow-listing strategy that adapts to user behavior patterns, leveraging the Classification and Regression Tree algorithm to identify peak usage periods.In our investigation, we discovered that implementing a time dynamic allow-listing strategy resulted in adversaries needing four to five times more scan attempts, on average, to uncover an open connection. This posed a significant challenge for attackers, as they were more likely to abandon their attempts if a connection consistently appeared closed. Additionally, our algorithmic findings revealed that our models demonstrated greater precision in predicting patterns for the upcoming week compared to monthly patterns. This disparity was attributed to substantial shifts in user behavior between different weeks of the months, with only minor changes observed within the same month.
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 | 0.000 | 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) | 0.002 | 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 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".