An Efficient and Truthful Leader IDS Election Mechanism for MANET
Bibliographic record
Abstract
In this paper, we consider the problem of increasing the effectiveness of an Intrusion Detection System (IDS) for a cluster of nodes in ad hoc networks. To solve such a prob- lem, a head cluster is elected by the nodes to handle the de- tection service. Current solution elects a leader randomly without considering the energy level of nodes. Such solu- tion is vulnerable to selfish nodes that do not provide IDS service to others while at the same time benefiting from oth- ers' services. From our experiments, selfish nodes reduce the effectiveness of an IDS since less packets are inspected over time. Here, we are modeling a distributed, truthful, and efficient mechanism for electing a leader IDS that han- dles the detection process in a cluster. Our solution is able to balance the energy among all the nodes and increase the overall lifetime of an IDS in a cluster. In our model, incen- tives are given in the form of reputation to encourage the nodes to cooperate in the leader election process. The rep- utation is used to track the cooperative behavior of nodes where miss-behaving nodes are punished by withholding the cluster's services. Reputations are calculated based on the truth-telling mechanism design known as Vickrey, Clarke, and Groves (VCG). Our analysis prove that truth-telling is the dominant strategy for all the nodes and therefore effi- ciency is guaranteed. Finally, simulation results show that our mechanism improves the performance of an IDS in an- alyzing packets and punishes misbehaving nodes.
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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 | 0.003 | 0.006 |
| 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 | 0.003 | 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".