A Guard Node (GN) based Technique against Misbehaving Nodes in MANET
Bibliographic record
Abstract
In open communication environment such as Ad hoc network, the possibility of having misbehaving nodes is high. The presence of misbehaving nodes could degrade the performance of the overall network. This mandates adopting Intrusion Detection System (IDS) that helps the routing protocol to avoid misbehavior nodes and links. The IDS should feature low overhead controlling packet, high accuracy level and low rate of both false alarms and missed detection rate. There are several IDS techniques proposed in the literature such as Watchdog and End-to-End acknowledgment based system. In this work, we propose a system based on assigning some nodes called “guard nodes” the responsibility of overhearing and reporting the misbehaving nodes. The scheme is proposed to overcome the majority of the drawbacks associated with the Watchdog techniques. We compare and evaluate our proposed scheme against Dynamic Source Routing (DSR) protocol using NS-2 program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".