A new adaptive evidential reasoning approach for network alarm correlation
Bibliographic record
Abstract
In computer networks, fault detection and identification techniques rely substantially on analyzing a set of observed alarms generated by different network entities due to unknown failures. However, network alarms are subject to becoming lost and spurious and their information is often incomplete, ambiguous, and inconsistent. In this paper, an adaptive distributed Dempster-Shafer evidential reasoning technique is proposed to effectively reduce the negative impact of the uncertainty properties which network alarms can exhibit. Each observed alarm is perceived as a piece of evidence and as such, the incomplete and ambiguous properties can be tackled within the framework of the evidential theory. A discounting mechanism by which the observed alarms are assigned certain weights is also presented. A given weight reflects the significance of the information in the corresponding alarm. Then, the alarms are correlated by the Dempster's rule of combination and the inconsistent alarms play a limited role in the alarm correlation process since they are given lower weights. Simulations confirm that the proposed scheme has a high detection rate even in the presence of defective alarms.
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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 | 0.004 | 0.011 |
| 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 | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".