Side Channel Monitoring: Packet Drop Attack Detection in Wireless Ad Hoc Networks
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
Wireless ad hoc networks have great potentials in a broad range of applications. Their inherent vulnerability to various network attacks however limits their wide adaptation and deployment in practice. In this paper we address one of the most dangerous attacks, packet drop attack, in wireless ad hoc networks by post-routing detection. We introduce a simple, effective detection technique Side Channel Monitoring (SCM). The idea is to use nodes adjacent to a data communication route to monitor the message forwarding behavior of the nodes en route. These monitoring nodes constitute a directional side channel toward the source, in parallel to the backward route (primary channel). On observing misbehavior, they issue alarm packets to the source node through both channels. Considering channel disconnectivity (topologically or due to malicious packet drop), we analytically study the security strength of SCM including detection rate and expected number of detected attacks. Numeric results show that it is effective in various network scenarios.
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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 | 0.000 | 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