Bibliographic record
Abstract
In Intrusion Detection Systems (IDSs) for Mobile Ad hoc NETworks (MANETs), IDS agents using local detection engines alone may lead to undesirable performance due to the dynamic feature of MANETs. In this paper, we present a nonoverlapping Zone-based Intrusion Detection System (ZBIDS) for MANETs. Focusing on the protection of MANET routing protocols, we propose the collaboration mechanism of ZBIDS agents and an aggregation algorithm used by ZBIDS gateway nodes. The aggregation algorithm mainly utilizes the probability distribution of the $Source$ attribute in order to make the final decisions to generate alarms. We demonstrate that, by integrating the security related information from a wider area, the aggregation algorithm can reduce the false alarm ratio and improve the detection ratio. Also, the gateway nodes in ZBIDS can provide more diagnostic information by presenting a global view of attacks. We also present an alert data model conformed to Intrusion Detection Message Exchange Format (IDMEF) to facilitate the interoperability of IDS agents. Based on the routing disruption attack aimed at the Dynamic Source Routing protocol (DSR), we study the performance of ZBIDS at different mobility levels. Simulation results show that our system can achieve lower false positive ratio and higher detection ratio, compared to systems with local detection only.
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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 | 0.000 | 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 | 0.000 | 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 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".