A system for detecting document leakage by insiders through continuous user authentication by using document reading behavior
Bibliographic record
Abstract
기업 내의 문서 유출을 탐지 제어하기 위한 다양한 기술들이 연구되고 있다. 하지만 이러한 기술들은 대부분 외부에 의한 문서 유출을 대상으로 하고 있으며, 문서에 대한 정당한 권한을 가지고 있는 내부자에 의한 문서 유출을 탐지하고 제어하는 연구는 미비한 수준이다. 본 연구에서는 내부자에 의한 문서 유출을 탐지하고 제어하기 위하여 사용자의 문서 읽기 행위를 관찰한다. Microsoft Word 로거에서 추출할 수 있는 속성으로부터 각 사용자의 관찰된 문서 읽기 행위에 대한 패턴을 만들고 시스템에 적용함으로써 문서를 읽고 있는 사용자가 실제 사용자인지 여부를 판단한다. 이를 통하여 사용자가 문서를 읽는 행위를 바탕으로 효과적으로 문서 유출을 방지할 수 있을 것으로 기대한다. There have been various techniques to detect and control document leakage; however, most techniques concentrate on document leakage by outsiders. There are rare techniques to detect and monitor document leakage by insiders. In this study, we observe user's document reading behavior to detect and control document leakage by insiders. We make each user's document reading patterns from attributes gathered by a logger program running on Microsoft Word, and then we apply the proposed system to help determine whether a current user who is reading a document matches the true user. We expect that our system based on document reading behavior can effectively prevent document leakage.
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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) | 0.000 | 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 | 0.004 |
| Open science | 0.001 | 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 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".