On the Effect of Black-hole Attack on Opportunistic Routing Protocols
Bibliographic record
Abstract
Black-hole is a well-known routing attack through which malicious nodes try to downgrade the communication performance of wireless networks. On the other hand, opportunistic routing protocols aim to increase the reliability of communications compared to traditional routing approaches by utilizing the broadcast nature of wireless medium. Although a tremendous amount of research has been performed in the literature to detect and cancel the effect of black-hole nodes for traditional routing protocols, it is of high importance to study the effects of such a significant security obstacle for opportunistic routing methods as well. In this paper, an analytical model is proposed using Markov chains to model the effect of black-hole attack on opportunistic routing protocols. Furthermore, a novel version of the black-hole attack is proposed and customized for opportunistic routing approaches. Finally, the consequences of this attack on delivering packets to their destination are evaluated, compared, and discussed using both analytical and simulation-based methods. Conducted analyses demonstrate that the proposed attack can have devastating effects on the network's performance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".