Using Logistic Trust for Event Learning and Misbehaviour Detection
Bibliographic record
Abstract
The advancement in communication technologies has enabled ad hoc networks to collect large volumes of information. This information is vulnerable to various types of attacks amongst which false information dissemination and on-off attacks offer biggest threats to the networks. As the data in ad hoc networks depends on the events, it is necessary for any detection mechanism to first determine the true events. Then the information about these events can be used to judge the behavior of the senders. Therefore, in this work, the correct event is first learned using information from different sources including the observations of the receiver itself. This information is later used to learn the behavior of the senders. The learned behavior combined with the opinions of the neighbors about the sender allows the detection of malicious and honest nodes. In this work, a logistic trust model is used to combine the observed behavior and opinions. It is observed that logistic trust results in a high accuracy of over 99% and a low error of less than 1% even when the events are changing rapidly. It is also shown that the scheme can detect malicious majority and identify true events with high probability.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".