FADE: Forwarding Assessment Based Detection of Collaborative Grey Hole Attacks in WMNs
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
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.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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