Detecting attacks in QoS-OLSR protocol
Bibliographic record
Abstract
In this paper, we detect two attacks targeting the QoS-OLSR protocol MANET. The Cluster-based model QoS-OLSR is a multimedia protocol designed on top of Optimized Link State Routing (OLSR) protocol. The quality of service (QoS) of the nodes is considered during the selection of the multi-point relays (MPRs) nodes. In this work, we identify two attacks that can be launched against the QoS-OLSR protocol: Identity spoofing attack, and wormhole attack. Watchdogs are used to detect the attacks performed by malicious nodes. As a solution, we propose to improve the watchdogs' detection by (1) using cooperative watchdog model and (2) adding the posterior belief function using Bayes' rule to the watchdog model. Simulation results show that the use of the Bayes' rule function along with the cooperative watchdog model improves the detection rate and reduces the false positives.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".