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 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 | 0.000 | 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".