A User Behavior-Based Approach to Detect the Insider Threat in Distributed Diagnostic Imaging Systems
Bibliographic record
Abstract
A modern diagnostic imaging system integrates several PACS (Picture Archiving and Communication System) through datacenters that allow a large community of users to access and share sensitive patient medical images. In such integration user access to the medical images that are stored in non-local PACS systems is based on a trust model, which makes data integrity and privacy vulnerable due to possible malicious user behaviors. Moreover, the limited scope and precision of the existing policy-based access control solutions prevent them from detecting suspicious behaviors of the authenticated users. In this paper, we propose an approach for analyzing the user behaviors that allows the administrators to identify the users whose behaviors may jeopardize the data privacy and system integrity. In this context, the system administrator can define an arbitrary pattern of a suspicious user behavior using our new behavior pattern language. A constraint-based pattern-matching engine will identify the instances of the suspicious behavior pattern in the system's audit-log repository. Finally, a decision support system will present the excerpt findings to the system administrator with the overall goal of refining the access control policy rules. We present a case study which indicates our proposed approach provides promising results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".