Exploiting channel-aware reputation system against selective forwarding attacks in WSNs
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are vulnerable to selective forwarding attacks that selectively drop a subset of the forwarding packets to degrade network performances. Due to unstable wireless channels, the packet loss rate between sensor nodes might be high, especially in hostile environments. Therefore, it is difficult to distinguish the malicious drop and normal packet loss. In this paper, we propose a Channel-aware deputation System (CRS) to identify selective forwarding misbehaviours from normal packet losses caused by poor channel quality or medium access collision. Specifically, CRS is based on normal packet loss estimation and neighbour monitoring. Each node maintains a reputation table to evaluate forwarding behaviours of its neighbours. Reputation value is determined by the deviation of the monitored packet loss rate and estimated normal loss rate. The nodes with reputation below a threshold are identified as misbehaving nodes and isolated from data forwarding paths. Furthermore, we develop weighted reputation propagation and integration functions to improve detection efficiency. Through theoretical analysis and extensive simulations, we demonstrate that CRS can accurately detect selective forwarding attacks and significantly improve the network throughput.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".