Defense against spectrum sensing data falsification attacks in mobile ad hoc networks with cognitive radios
Bibliographic record
Abstract
Cognitive radios (CRs) have been considered for use in mobile ad hoc networks (MANETs). The area of security in Cognitive Radio MANETs (CR-MANETs) has yet to receive much attention. However, some distinct characteristics of CRs introduce new, non-trivial security risks to CR-MANETs. In this paper, we study spectrum sensing data falsification (SSDF) attacks to CR-MANETs, in which intruders send false local spectrum sensing results in cooperative spectrum sensing, and SSDF may result in incorrect spectrum sensing decisions by CRs. We present a consensus-based cooperative spectrum sensing scheme to counter SSDF attacks in CR-MANETs. Our scheme is based on recent advances in consensus algorithms that have taken inspiration from self-organizing behavior of animal groups such as fish. Unlike the existing schemes, there is no need for a common receiver to do the data fusion for reaching the final decision to counter SSDF attacks. Simulation results are presented to show the effectiveness of the proposed scheme.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".