Detection and Prevention of Denial of Service (DoS) Attacks in Mobile Ad Hoc Networks using Reputation-based Incentive Schemes
Bibliographic record
Abstract
Mobile ad hoc networks (MANETs) are dynamic mobile networks that can be formed in the absence of any pre-existing communication infrastructure. In addition to node mobility, a MANET is characterized by limited resources such as bandwidth, battery power, and storage space. The underlying assumption in MANETs is that the intermediate nodes cooperate in forwarding packets. However, this assumption does not hold in commercial and emerging civilian applications. MANETs are vulnerable to Denial of Service (DoS) due to their salient characteristics. There is a need to provide an incentive mechanism that can provide cooperation among nodes in the network and improve overall network performance by reducing DoS attacks. In this paper, we propose a reputation-based incentive mechanism for detecting and preventing DoS attacks. DoS attacks committed by selfish and malicious nodes were investigated. Our scheme motivates nodes to cooperate and excludes them from the network only if they fail to do so. We evaluated the performance of our scheme using the packet delivery ratio, the routing and communication overhead, and misbehaving node detection in a discrete event-simulation environment. The results indicate that a reputation-based incentive mechanism can significantly reduce the effect of DoS attacks and improve performance in MANETs.
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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 | 0.006 | 0.014 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".