Defense against spectrum sensing data falsification attacks in mobile ad hoc networks with cognitive radios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
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 it