Structural Results for Combined Continuous User Authentication and Intrusion Detection in High Security Mobile Ad-Hoc Networks
Why this work is in the frame
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Bibliographic record
Abstract
Continuous user authentication is an important prevention-based approach to protect high security mobile ad-hoc networks (MANETs). On the other hand, intrusion detection systems (IDSs) are also important in MANETs to effectively identify malicious activities. Considering these two approaches jointly is effective in optimal security design taking into account system security requirements and resource constraints in MANETs. To obtain the optimal scheme of combining continuous user authentication and IDSs in a distributed manner, we formulate the problem as a partially observable Markov decision process (POMDP) multi-armed bandit problem. We present a structural results method to solve the problem for a large network with a variety of nodes. The policies derived from structural results are easy to implement in practical MANETs. Simulation results are presented to show the effectiveness and the performance of the proposed scheme.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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