Towards an Uncertainty-aware Decision Engine for Proactive Self-Protecting Software
Bibliographic record
Abstract
Proactive protection of software systems can be achieved through Moving Target Defense (MTD) techniques, which are designed based on addressing the questions of what to move, how to move, and when to move. However, the deployment of MTD techniques is subject to overhead and run-time uncertainties that can impact their effectiveness in defending against cyberattacks. The goal in this research is to improve the uncertainty-awareness and self-adaptation in a MTD decision engine in order to enhance the effectiveness of MTD techniques. The proposed approach is based on using the Partially Observable Markov Decision Process framework to quantify uncertainties. A systematic approach is proposed to achieve this objective, which includes modelling, designing, and engineering phases. The outcome of this research will make contributions related to (i) modelling solutions for run-time uncertainty, (ii) designing scalable methods for uncertainty-aware MTD planning, and (iii) realizing an uncertainty-aware MTD decision engine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".