A Game-Theoretic Framework for Robust Optimal Intrusion Detection in Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
A robust optimization model is considered for nonzero-sum discounted stochastic games with incomplete information in order to formally formulate and analyze the intrusion detection problem in wireless sensor networks (WSNs). Security requirements of WSNs are taken into account to characterize the game parameters and model the player objectives. To generalize the problem, the game data are assumed not to be fully known to the players, who take a robust optimization approach to address this data uncertainty. For assessing the validity and effectiveness of the framework, illustrative instances of the developed game model are generated. Equilibrium analysis reveals how the conflicting objectives of the intruder and intrusion detection system compel them to adopt different conservative stances toward data uncertainty. It is also shown, by numerical results, that the robust approach in the presence of uncertainty reduces the sensitivity of the solution with respect to data perturbations, and thus improves design stability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it