FADE: Forwarding Assessment Based Detection of Collaborative Grey Hole Attacks in WMNs
Bibliographic record
Abstract
Data security, which is concerned with the confidentiality, integrity and availability of data, is still challenging the application of wireless mesh networks (WMNs). In this paper, we focus on a special type of denial-of-service attack, called selective forwarding or grey hole attack. When this attack is launched at the gateways of a WMN where data tend to aggregate, it could lead to severe damages due to loss of sensitive data. Most existing proposals that focus on detecting stand-alone attackers via channel overhearing are ineffective against collusive attackers. In this paper, we propose a forwarding assessment based detection (FADE) scheme to mitigate collaborative grey hole attacks. Specifically, FADE detects sophisticated attacks by means of forwarding assessments aided by two-hop acknowledgement monitoring. Moreover, FADE can coexist with contemporary link security techniques. We analyze the optimal detection threshold that minimizes the sum of false positive rate and false negative rate of FADE, considering the network dynamics due to degraded channel quality or medium access collisions. Extensive simulation results are presented to demonstrate the adaptability of FADE to network dynamics and its effectiveness in detecting collaborative grey hole attacks.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".