A Self-Adaptive Detection System for MAC Misbehavior in Ad Hoc Networks
Bibliographic record
Abstract
MAC layer misbehavior due to selfish or malicious reasons can significantly degrade the performance of mobile adhoc networks. Currently, detection systems for handling selfish misbehavior has been proposed and studied. In this paper we study a new class of malicious misbehaviors that causes transmission timeout of MAC frames at either the transmitter side or the receiver side. A misbehaving node fully cooperates by forwarding packets for other nodes and completely adheres to the proper selection of backoff intervals; however, it maliciously forces the forwarding operation to fail in order to either disrupt the route discovery process or cause damage to the existing flows routed through itself. We design and implement a new detection system that identifies the malicious nodes through a set of monitoring and reaction procedures. Once a misbehaving node is detected, the system reacts, by adapting simple protocol parameters, to mitigate the negative effects. We describe the detection system and the different reaction procedures for different misbehaviors. We evaluate through network simulation the effectiveness of the system in detecting malicious nodes and improving the network performance.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 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".