Severity analysis and countermeasure for the wormhole attack in wireless ad hoc networks
Bibliographic record
Abstract
In this paper, we analyze the effect of the wormhole attack on shortest-path routing protocols for wireless ad hoc networks. Using analytical and simulation results, we show that a strategic placement of the wormhole when the nodes are uniformly distributed can disrupt/control on average 32% of all communications across the network. We also analyze a scenario in which several attackers make wormholes between each other and a case where two malicious nodes attack a target node in the network. We show how to evaluate the maximum effect of the wormhole attack on a given network topology. Then, we compute the maximum effect of the wormhole attack on grid topology networks and show that the attackers can disrupt/control around 40% to 50% of all communications when the wormhole is strategically placed in the network. Finally, to defend against the wormhole attack, we propose a timing-based countermeasure that avoids the deficiencies of existing timing-based solutions. Using the proposed countermeasure, the nodes do not need synchronized clocks, nor are they required to predict the sending time or to be capable of fast switching between the receive and send modes. Moreover, the nodes do not need one-to-one communication with all their neighbors and do not require to compute a signature while having to timestamp the message with its transmission time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".