A Cooperative Approach for Analyzing Intrusions in Mobile Ad hoc Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider the problem of reducing the number of false positives generated by cooperative intrusion detection systems (IDSs) in mobile ad hoc networks (MANETs). We define a flexible scheme using security classes, where an IDS is able to operate in different modes at each security class. This scheme helps in minimizing false alarms and informing the prevention system accurately about the severity of an intrusion. Shapley value is used to formally express the cooperation among all the nodes. To the best of our knowledge, there has not been any study for the case where the intrusions in MANETs are analyzed, in order to decrease false positives, using cooperative game theory. Our game theoretic model assists in analyzing the contribution of each mobile node on each security class in order to decrease false positives taking into consideration the reputation of nodes. Simulation results are given to validate the efficiency of our model in detecting intrusions and reducing false positives.
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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 | 0.001 | 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 | 0.000 |
| 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 it