Wormhole attack detection based on distance verification and the Use of hypothesis testing for wireless ad hoc networks
Bibliographic record
Abstract
In this paper, a technique for detection of wormhole attacks based on distance verification is proposed for mobile ad hoc network (MANETs) applications. A node estimates its distances to a sender node based on the received signal strength (RSS) of received packets, and uses them to verify against the distances computed from the location information in the packets. The verification is formulated as a hypothesis testing problem and a Neyman-Pearson approach is used to decide whether the sender node is under wormhole attack or not. An implementation of the optimized link state routing (OLSR) protocol is discussed. A simple collaborative decision-making strategy is proposed to counter the limitations of distance verification by a single node. The proposed technique is computationally efficient. It is able to provide statistical performance measures for the detection results, an important component that has been missing in existing wormhole detection techniques. Finally, computer simulations are used to demonstrate the effectiveness and performance of the proposed technique.
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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 | 0.000 |
| 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".