MANET security through a distributed policy-based evaluation of node behaviour
Bibliographic record
Abstract
While MANETs can provide seamless networking that is fast and easy to deploy, the lack of a stable topology, no centralized control, and the use of wireless links with limited bandwidth make MANETs highly vulnerable to attacks. The autonomy of individual nodes must be balanced with the need to ensure that nodes contribute to the network function as a whole. Trust-based systems are often proposed in this situation, but the mechanisms by which trust are built is still an active area of research. In this paper we present a policy-based trust management framework that uses the observed behaviour of neighbouring MANET nodes to identify and react to attacks. The system uses policy rules to evaluate observed behaviour of neighbouring nodes and reputation values shared by peers. The calculated trust values are then sent to neighbours. The calculation of trust based on a combination of flexible policy rules and distributed evaluation is a novel approach and a significant contribution of the paper. A simulation-based evaluation of our framework based on a wormhole attack shows that the scheme is both scalable and robust.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".