A Systematic Approach for Privilege Escalation Prevention
Bibliographic record
Abstract
Information systems are designed to present services and functionalities for multiple users. Thus, it is used to have on one information system different levels of privilege for different users. Privileges describe what a user is permitted to do such as viewing files, modifying or deleting data. Privilege escalation takes place when a user gets access to more resources or services than they are normally allowed to perform unauthorized actions. Many studies have been presented to detect anomalies and vulnerabilities in information systems to discover security issues or attacks related to privilege escalation. In this context, we introduced a systematic methodology that uses pattern recognition and outliers identification to detect numerous abnormal events which can indicate anomalies and security issues related to privilege escalation. In this paper, we describe results from an empirical study in order to show the performance of a new outliers detection algorithm to identify unknown abnormal events and the ability of pattern recognition techniques to specify known abnormal events. Results show that the outlier detection algorithm, enables us to discover unknown abnormal events on synthetic data sets. In addition, results notice the existence and the usefulness of four patterns used the discover known privilege escalation scenarios and, potentially, reduce security issues in systems.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".