Enhancement of frequency-based wormhole attack detection
Bibliographic record
Abstract
Mobile Ad Hoc Networks (MANETs) have been seen as a key tactical communication technology. However, one of the most severe attacks in MANETs, the wormhole attack remains a sizable challenge. Most existing wormhole detection techniques rely on specialized hardware such as directional antennas, GPS, or high precision clocks, which can limit their efficacy. In order to provide an efficient and accurate detection mechanism for wormhole attacks, we present a new method based on signal processing techniques, in which purposely shaped traffic is transmitted, analysed at the destination node by constructing the reception time data into a “signal”, and then transforming this signal to the frequency domain using the Fast Fourier Transform (FFT). Using this technique, the wormhole attack can be quickly and accurately identified. We demonstrate in simulation and in a testbed that the proposed methodology can be used to detect an attack within seconds. In addition, the detection mechanism proposed is agnostic of routing protocol and does not require any specialized hardware support.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".