JamRF: Performance Analysis, Evaluation, and Implementation of RF Jamming over Wi-Fi
Why this work is in the frame
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Bibliographic record
Abstract
Jamming attacks significantly degrade the performance of wireless communication systems and can lead to significant overhead in terms of re-transmissions and increased power consumption. Although different jamming techniques are discussed in the literature, numerous open-source implementations have used expensive equipment in the range of thousands of dollars with the exception of a few. These implementations have also tended to be partial band, and do not cover the whole available bandwidth of the system under attack. In this work, we demonstrate that flexible, reliable, and low priced software-defined radio (SDR) jamming is feasible by designing and implementing different types of jammers against IEEE 802.11n networks. First, to demonstrate the optimal jamming waveform, we present an analytical bit error rate expression of the system under attack by employing two common jamming waveforms: Gaussian noise and digitally modulated. Then, we validate this analysis through simulations using the MATLAB WLAN toolbox. Afterwards, we implement JamRF, a toolkit that employs a low-cost SDR to implement numerous types of jammers to validate the analysis. Obtained results showed that, to jam the whole 2.4GHz spectrum, a stateful-reactive jammer employing random channel hopping jamming strategy, achieves a packet loss ratio above 90%.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it