A Secure MANET Routing Protocol with Resilience against Byzantine Behaviours of Malicious or Selfish Nodes
Bibliographic record
Abstract
Secure routing in mobile ad hoc networks (MANETs) has emerged as a important MANET research area. MANETs, by virtue of the fact that they are wireless networks, are more vulnerable to intrusion by malicious agents than wired networks. In wired networks, appropriate physical security measures, such as restriction of physical access to network infrastructures, can be used to attenuate the risk of intrusions. Physical security measures are less effective however in limiting access to wireless network media. Consequently, MANETs are much more susceptible to infiltration by malicious agents. Authentication mechanisms can help to prevent unauthorized access to MANETs. However, considering the high likelihood that nodes with proper authentication credentials can be taken over by malicious entities, there are needs for security protocols which allow MANET nodes to operate in potential adversarial environments. In this paper, we present a secure on-demand MANET routing protocol, we named Robust Source Routing (RSR). In addition to providing data origin authentication services and integrity checks, RSR is able to mitigate against intelligent malicious agents which selectively drop or modify packets they agreed to forward. Simulation studies confirm that RSR is capable of maintaining high delivery ratio even when a majority of the MANET nodes are malicious.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
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".