Routing anomaly detection in mobile ad hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
Intrusion detection systems (IDSs) for mobile ad hoc networks (MANETs) are necessary when we deploy MANETs in reality. In this paper, focusing on the protection of MANET routing protocols, we present a new intrusion detection agent model and utilize a Markov chain based anomaly detection algorithm to construct the local detection engine. The details of feature selection, data collection, data preprocess, Markov chain construction, classifier construction and parameter tuning are provided. Based on the routing disruption attack aimed at the dynamic source routing protocol (DSR), we study the performance of the algorithm at different mobility levels. Simulation results show that our algorithm can achieve low false positive ratio, high detection ratio, and small MTFA (mean time to the first alarm), especially when the mobility is low. Detailed analysis of simulation results is also presented.
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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 | 0.000 |
| 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